# The Wisdom of a Crowd of Brains: A Universal Brain Encoder

arXiv: https://arxiv.org/abs/2406.12179 · HTML: https://mlsanity.com/paper/2406.12179/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 58 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 17 of 17)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors](https://mlsanity.com/paper/2305.18274/) (2023)
- [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)
- [From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI](https://mlsanity.com/paper/1907.02431/) (2019)
- [MindBridge: A Cross-Subject Brain Decoding Framework](https://mlsanity.com/paper/2404.07850/) (2024)
- [Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision](https://mlsanity.com/paper/1608.03425/) (2016)
- [The Algonauts Project 2023 Challenge: How the Human Brain Makes Sense of Natural Scenes](https://mlsanity.com/paper/2301.03198/) (2023)
- [Brain encoding models based on multimodal transformers can transfer across language and vision](https://mlsanity.com/paper/2305.12248/) (2023)
- [MindTuner: Cross-Subject Visual Decoding with Visual Fingerprint and Semantic Correction](https://mlsanity.com/paper/2404.12630/) (2024)
- [Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models](https://mlsanity.com/paper/2306.03089/) (2023)
- [BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity](https://mlsanity.com/paper/2310.04420/) (2023)
- [Personalized visual encoding model construction with small data](https://mlsanity.com/paper/2202.02245/) (2022)
- [Learning shared neural manifolds from multi-subject FMRI data](https://mlsanity.com/paper/2201.00622/) (2021)
- [A shared neural encoding model for the prediction of subject-specific fMRI response](https://mlsanity.com/paper/2006.15802/) (2020)
- [Functional Brain-to-Brain Transformation with No Shared Data](https://mlsanity.com/paper/2404.11143/) (2024)

## Built on by (most cited first, 15 of 15)

- [MindSimulator: Exploring Brain Concept Localization via Synthetic FMRI](https://mlsanity.com/paper/2503.02351/) (2025)
- [A foundation model of vision, audition, and language for in-silico neuroscience](https://mlsanity.com/paper/2605.04326/) (2026)
- [SynBrain: Enhancing Visual-to-fMRI Synthesis via Probabilistic Representation Learning](https://mlsanity.com/paper/2508.10298/) (2025)
- [Brain-IT: Image Reconstruction from fMRI via Brain-Interaction Transformer](https://mlsanity.com/paper/2510.25976/) (2025)
- [BrainExplore: Large-Scale Discovery of Interpretable Visual Representations in the Human Brain](https://mlsanity.com/paper/2512.08560/) (2025)
- [Meta-Learning an In-Context Transformer Model of Human Higher Visual Cortex](https://mlsanity.com/paper/2505.15813/) (2025)
- [Brain-tuning Improves Generalizability and Efficiency of Brain Alignment in Speech Models](https://mlsanity.com/paper/2510.21520/) (2025)
- [Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding](https://mlsanity.com/paper/2604.08537/) (2026)
- [Beyond Grid-Locked Voxels: Neural Response Functions for Continuous Brain Encoding](https://mlsanity.com/paper/2510.07342/) (2025)
- [ViBE: Visual-to-M/EEG Brain Encoding via Spatio-Temporal VAE and Distribution-Aligned Projection](https://mlsanity.com/paper/2604.26218/) (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)
- [MindAlign: Bridging EEG, Vision, and Language for Zero-Shot Visual Decoding](https://mlsanity.com/paper/2605.24523/) (2026)
- [Brain-IT-VQA: From Brain Signals to Answers](https://mlsanity.com/paper/2605.29588/) (2026)
- [Platonic Projection Structures: Operator-Induced Observability in Representation Learning](https://mlsanity.com/paper/2607.05175/) (2026)

## Abstract (arXiv)

Image-to-fMRI encoding is important for both neuroscience research and practical applications. However, such "Brain-Encoders" have been typically trained per-subject and per fMRI-dataset, thus restricted to very limited training data. In this paper we propose a Universal Brain-Encoder, which can be trained jointly on data from many different subjects/datasets/machines. What makes this possible is our new voxel-centric Encoder architecture, which learns a unique "voxel-embedding" per brain-voxel. Our Encoder trains to predict the response of each brain-voxel on every image, by directly computing the cross-attention between the brain-voxel embedding and multi-level deep image features. This voxel-centric architecture allows the functional role of each brain-voxel to naturally emerge from the voxel-image cross-attention. We show the power of this approach to (i) combine data from multiple different subjects (a "Crowd of Brains") to improve each individual brain-encoding, (ii) quick & effective Transfer-Learning across subjects, datasets, and machines (e.g., 3-Tesla, 7-Tesla), with few training examples, and (iii) use the learned voxel-embeddings as a powerful tool to explore brain functionality (e.g., what is encoded where in the brain).