# TopoNets: High Performing Vision and Language Models with Brain-Like Topography

arXiv: https://arxiv.org/abs/2501.16396 · HTML: https://mlsanity.com/paper/2501.16396/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2025.

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

- [Brain-Model Evaluations Need the NeuroAI Turing Test](https://mlsanity.com/paper/2502.16238/) (2025)
- [Zero-Shot Performance Prediction for Probabilistic Scaling Laws](https://mlsanity.com/paper/2510.16743/) (2025)
- [Are Neuro-Inspired Multi-Modal Vision-Language Models Resilient to Membership Inference Privacy Leakage?](https://mlsanity.com/paper/2511.20710/) (2025)
- [Discovering Functionally Selective Brain Regions with a Deep Topographic Multimodal Model](https://mlsanity.com/paper/2606.09770/) (2026)
- [Beyond topography: Topographic regularization improves robustness and reshapes representations in convolutional neural networks](https://mlsanity.com/paper/2508.00043/) (2025)
- [Topographic Constraints Shape Brain-Like Component Structure in Auditory Models](https://mlsanity.com/paper/2509.24039/) (2025)
- [Model-Guided Microstimulation Steers Primate Visual Behavior](https://mlsanity.com/paper/2510.03684/) (2025)
- [Semantic representations emerge in biologically inspired ensembles of cross-supervising neural networks](https://mlsanity.com/paper/2510.14486/) (2025)
- [Learning Glioblastoma Tumor Heterogeneity Using Brain Inspired Topological Neural Networks](https://mlsanity.com/paper/2602.11234/) (2026)
- [Topographic Training Concentrates Causal Circuits Without Improving Neuron Monosemanticity](https://mlsanity.com/paper/2609.24379/) (2026)

## Abstract (arXiv)

Neurons in the brain are organized such that nearby cells tend to share similar functions. AI models lack this organization, and past efforts to introduce topography have often led to trade-offs between topography and task performance. In this work, we present TopoLoss, a new loss function that promotes spatially organized topographic representations in AI models without significantly sacrificing task performance. TopoLoss is highly adaptable and can be seamlessly integrated into the training of leading model architectures. We validate our method on both vision (ResNet-18, ResNet-50, ViT) and language models (GPT-Neo-125M, NanoGPT), collectively TopoNets. TopoNets are the highest-performing supervised topographic models to date, exhibiting brain-like properties such as localized feature processing, lower dimensionality, and increased efficiency. TopoNets also predict responses in the brain and replicate the key topographic signatures observed in the brain's visual and language cortices. Together, this work establishes a robust and generalizable framework for integrating topography into leading model architectures, advancing the development of high-performing models that more closely emulate the computational strategies of the human brain.