# End-to-end topographic networks as models of cortical map formation and human visual behaviour: moving beyond convolutions

arXiv: https://arxiv.org/abs/2308.09431 · HTML: https://mlsanity.com/paper/2308.09431/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 78 references that are in the map.
- First posted on arXiv 18 Aug 2023.

## Builds on (most cited first, 4 of 4)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [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)
- [The neuroconnectionist research programme](https://mlsanity.com/paper/2209.03718/) (2022)
- [A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs](https://mlsanity.com/paper/1901.00945/) (2019)

## Built on by (most cited first, 8 of 8)

- [TopoLM: brain-like spatio-functional organization in a topographic language model](https://mlsanity.com/paper/2410.11516/) (2024)
- [Achieving More Human Brain-Like Vision via Human EEG Representational Alignment](https://mlsanity.com/paper/2401.17231/) (2024)
- [Teaching CORnet Human fMRI Representations for Enhanced Model-Brain Alignment](https://mlsanity.com/paper/2407.10414/) (2024)
- [Discovering Functionally Selective Brain Regions with a Deep Topographic Multimodal Model](https://mlsanity.com/paper/2606.09770/) (2026)
- [Topoformer: brain-like topographic organization in Transformer language models through spatial querying and reweighting](https://mlsanity.com/paper/2510.18745/) (2025)
- [Foveated Retinotopy Improves Classification and Localization in Convolutional Neural Networks](https://mlsanity.com/paper/2402.15480/) (2024)
- [Language learning shapes visual category-selectivity in deep neural networks](https://mlsanity.com/paper/2502.16456/) (2025)
- [Model-Guided Microstimulation Steers Primate Visual Behavior](https://mlsanity.com/paper/2510.03684/) (2025)

## Abstract (arXiv)

Computational models are an essential tool for understanding the origin and functions of the topographic organisation of the primate visual system. Yet, vision is most commonly modelled by convolutional neural networks that ignore topography by learning identical features across space. Here, we overcome this limitation by developing All-Topographic Neural Networks (All-TNNs). Trained on visual input, several features of primate topography emerge in All-TNNs: smooth orientation maps and cortical magnification in their first layer, and category-selective areas in their final layer. In addition, we introduce a novel dataset of human spatial biases in object recognition, which enables us to directly link models to behaviour. We demonstrate that All-TNNs significantly better align with human behaviour than previous state-of-the-art convolutional models due to their topographic nature. All-TNNs thereby mark an important step forward in understanding the spatial organisation of the visual brain and how it mediates visual behaviour.