# 元学习上下文学习实现免训练跨受试者脑解码

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-09 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmo82l6vp03lkslmljx2i9wl7
- 原文链接：https://arxiv.org/abs/2604.08537

## AI 摘要

研究团队提出一种元优化的fMRI语义视觉解码方法，通过上下文学习实现免训练的跨受试者泛化。该方法仅需少量图像-脑激活样本作为条件，即可快速推断新受试者的独特神经编码模式，并采用分层反演策略完成解码。实验表明，无需重新训练、微调、解剖对齐或刺激重叠，即可在多种视觉主干网络上实现强跨受试者和跨扫描仪泛化能力，为构建非侵入式脑解码通用基础模型奠定关键基础。

## 正文

Visual decoding from brain signals is a key challenge at the intersection of computer vision and neuroscience, requiring methods that bridge neural representations and computational models of vision. A field-wide goal is to achieve generalizable, cross-subject models. A major obstacle towards this goal is the substantial variability in neural representations across individuals, which has so far required training bespoke models or fine-tuning separately for each subject. To address this challenge, we introduce a meta-optimized approach for semantic visual decoding from fMRI that generalizes to novel subjects without any fine-tuning. By simply conditioning on a small set of image-brain activation examples from the new individual, our model rapidly infers their unique neural encoding patterns to facilitate robust and efficient visual decoding. Our approach is explicitly optimized for in-context learning of the new subject's encoding model and performs decoding by hierarchical inference, inverting the encoder. First, for multiple brain regions, we estimate the per-voxel visual response encoder parameters by constructing a context over multiple stimuli and responses. Second, we construct a context consisting of encoder parameters and response values over multiple voxels to perform aggregated functional inversion. We demonstrate strong cross-subject and cross-scanner generalization across diverse visual backbones without retraining or fine-tuning. Moreover, our approach requires neither anatomical alignment nor stimulus overlap. This work is a critical step towards a generalizable foundation model for non-invasive brain decoding.