# BrainExplore: Large-Scale Discovery of Interpretable Visual Representations in the Human Brain

arXiv: https://arxiv.org/abs/2512.08560 · HTML: https://mlsanity.com/paper/2512.08560/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 62 references that are in the map.
- First posted on arXiv 9 Dec 2025.

## Builds on (most cited first, 20 of 20)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Sparse Autoencoders Find Highly Interpretable Features in Language Models](https://mlsanity.com/paper/2309.08600/) (2023)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Network Dissection: Quantifying Interpretability of Deep Visual Representations](https://mlsanity.com/paper/1704.05796/) (2017)
- [Interpreting CLIP's Image Representation via Text-Based Decomposition](https://mlsanity.com/paper/2310.05916/) (2023)
- [CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks](https://mlsanity.com/paper/2204.10965/) (2022)
- [Natural Language Descriptions of Deep Visual Features](https://mlsanity.com/paper/2201.11114/) (2022)
- [Generic decoding of seen and imagined objects using hierarchical visual features](https://mlsanity.com/paper/1510.06479/) (2015)
- [MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data](https://mlsanity.com/paper/2403.11207/) (2024)
- [A Multimodal Automated Interpretability Agent](https://mlsanity.com/paper/2404.14394/) (2024)
- [The Algonauts Project 2023 Challenge: How the Human Brain Makes Sense of Natural Scenes](https://mlsanity.com/paper/2301.03198/) (2023)
- [Interpreting the Second-Order Effects of Neurons in CLIP](https://mlsanity.com/paper/2406.04341/) (2024)
- [FIND: A Function Description Benchmark for Evaluating Interpretability Methods](https://mlsanity.com/paper/2309.03886/) (2023)
- [Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models](https://mlsanity.com/paper/2306.03089/) (2023)
- [NeuroGen: activation optimized image synthesis for discovery neuroscience](https://mlsanity.com/paper/2105.07140/) (2021)
- [The Wisdom of a Crowd of Brains: A Universal Brain Encoder](https://mlsanity.com/paper/2406.12179/) (2024)
- [Brain Mapping with Dense Features: Grounding Cortical Semantic Selectivity in Natural Images With Vision Transformers](https://mlsanity.com/paper/2410.05266/) (2024)
- [In Silico Mapping of Visual Categorical Selectivity Across the Whole Brain](https://mlsanity.com/paper/2510.21142/) (2025)
- [Brain-IT: Image Reconstruction from fMRI via Brain-Interaction Transformer](https://mlsanity.com/paper/2510.25976/) (2025)
- [Functional Brain-to-Brain Transformation with No Shared Data](https://mlsanity.com/paper/2404.11143/) (2024)

## Built on by (most cited first, 5 of 5)

- [Sparse Autoencoders Map Brain-LLM Alignment onto Cortical Semantic Topography](https://mlsanity.com/paper/2605.23035/) (2026)
- [Mechanistically Interpretable Neural Encoding Reveals Fine-Grained Functional Selectivity in Human Visual Cortex](https://mlsanity.com/paper/2605.16468/) (2026)
- [From Activation to Specificity: Automating Counterfactual Testing of Visual Representations in the Human Brain](https://mlsanity.com/paper/2605.23895/) (2026)
- [Brain-IT-VQA: From Brain Signals to Answers](https://mlsanity.com/paper/2605.29588/) (2026)
- [Emergent Hierarchical Monosemantic Neurons from the Group-Contrastive Forward-Forward Algorithm](https://mlsanity.com/paper/2607.16295/) (2026)

## Abstract (arXiv)

Understanding how the human brain represents visual concepts, and in which brain regions these representations are encoded, remains a long-standing challenge. Decades of work have advanced our understanding of visual representations, yet brain signals remain large and complex, and the space of possible visual concepts is vast. As a result, most studies remain small-scale, rely on manual inspection, focus on specific regions and concepts, and rarely include systematic validation. We present a large-scale, automated framework for discovering and explaining visual representations across the human cortex. Our method comprises two main stages. First, we discover candidate interpretable patterns in fMRI activity through unsupervised, data-driven decomposition methods. Next, we explain each pattern by identifying the set of natural images that most strongly elicit it and generating a natural-language description of their shared visual meaning. To scale this process, we introduce an automated pipeline that tests multiple candidate explanations, assigns reliability scores, and selects the best description for each voxel pattern. Our framework reveals thousands of interpretable patterns spanning many distinct visual concepts, including fine-grained representations previously unreported.