# A Unified Geometric Space for Topological Alignment Between Transformer-Based Models and Human Brain Networks

arXiv: https://arxiv.org/abs/2510.24342 · HTML: https://mlsanity.com/paper/2510.24342/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 68 references that are in the map.
- First posted on arXiv 28 Oct 2025.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019)
- [How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers](https://mlsanity.com/paper/2106.10270/) (2021)
- [DeiT III: Revenge of the ViT](https://mlsanity.com/paper/2204.07118/) (2022)
- [The Platonic Representation Hypothesis](https://mlsanity.com/paper/2405.07987/) (2024)
- [BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers](https://mlsanity.com/paper/2208.06366/) (2022)
- [Getting aligned on representational alignment](https://mlsanity.com/paper/2310.13018/) (2023)
- [Language Modeling with Deep Transformers](https://mlsanity.com/paper/1905.04226/) (2019)
- [Instruction-tuning Aligns LLMs to the Human Brain](https://mlsanity.com/paper/2312.00575/) (2023)
- [Contextual Feature Extraction Hierarchies Converge in Large Language Models and the Brain](https://mlsanity.com/paper/2401.17671/) (2024)
- [Human-like object concept representations emerge naturally in multimodal large language models](https://mlsanity.com/paper/2407.01067/) (2024)
- [Latent Positional Information is in the Self-Attention Variance of Transformer Language Models Without Positional Embeddings](https://mlsanity.com/paper/2305.13571/) (2023)
- [From Colors to Classes: Emergence of Concepts in Vision Transformers](https://mlsanity.com/paper/2503.24071/) (2025)
- [Small-World Brain Networks Revisited](https://mlsanity.com/paper/1608.05665/) (2016)
- [Disentangling the Factors of Convergence between Brains and Computer Vision Models](https://mlsanity.com/paper/2508.18226/) (2025)
- [Dissecting Query-Key Interaction in Vision Transformers](https://mlsanity.com/paper/2405.14880/) (2024)
- [Better artificial intelligence does not mean better models of biology](https://mlsanity.com/paper/2504.16940/) (2025)
- [Coupling Artificial Neurons in BERT and Biological Neurons in the Human Brain](https://mlsanity.com/paper/2303.14871/) (2023)
- [A Unified and Biologically-Plausible Relational Graph Representation of Vision Transformers](https://mlsanity.com/paper/2206.11073/) (2022)
- [Positional Attention: Expressivity and Learnability of Algorithmic Computation](https://mlsanity.com/paper/2410.01686/) (2024)
- [Coupling Visual Semantics of Artificial Neural Networks and Human Brain Function via Synchronized Activations](https://mlsanity.com/paper/2206.10821/) (2022)
- [Bridging Brains and Machines: A Unified Frontier in Neuroscience, Artificial Intelligence, and Neuromorphic Systems](https://mlsanity.com/paper/2507.10722/) (2025)
- [Hierarchical Frequency Tagging Probe (HFTP): A Unified Approach to Investigate Syntactic Structure Representations in Large Language Models and the Human Brain](https://mlsanity.com/paper/2510.13255/) (2025)

## Abstract (arXiv)

Whether artificial neural networks organize information comparably to the human brain remains unclear. Prior brain--AI alignment studies are constrained by specific inputs and tasks, limiting cross-modal comparison. Here we introduce a brain--model topological alignment space, mapping Transformer attention topology onto human intrinsic connectivity networks (ICNs) to enable task-free, modality-agnostic comparison. Analyzing 151 Transformer-based models with 62,480 attention head graphs, we observe a continuous arc-shaped distribution reflecting varying alignment. Models optimized for global semantics aligned with higher-order ICNs, while local-detail models aligned with sensory ICNs. Non-intuitive findings include reduced alignment in DINOv2 compared to its predecessors and a counterintuitive scaling inversion in distilled DeiT models, while fine-tuning and instruction tuning had limited effect. Alignment scores showed no significant correlation with ImageNet accuracy (r = 0.266, p = 0.156). This work offers a quantitative framework for comparing the organizational principles of artificial and biological systems.