Brain encoding and decoding aims to understand the relationship between external stimuli and brain activities, and is a fundamental problem in neuroscience. In this article, we study latent embedding alignment for brain encoding and decoding, with a focus on improving sample efficiency under limited fMRI-stimulus paired data and substantial subject heterogeneity. We propose a lightweight alignment framework equipped with two statistical learning components: inverse semi-supervised learning that leverages abundant unpaired stimulus embeddings through inverse mapping and residual debiasing, and meta transfer learning that borrows strength from pretrained models across subjects via sparse aggregation and residual correction. Both methods operate exclusively at the alignment stage while keeping encoders and decoders frozen, allowing for efficient computation, modular deployment, and rigorous theoretical analysis. We establish finite-sample generalization bounds and safety guarantees, and demonstrate competitive empirical performance on the large-scale fMRI-image reconstruction benchmark data.

翻译：脑编码与解码旨在理解外部刺激与大脑活动之间的关系，是神经科学中的基本问题。本文研究面向脑编码与解码的潜在嵌入对齐，重点关注在fMRI-刺激配对数据有限且被试个体异质性显著的条件下提升样本效率。我们提出了一个轻量级对齐框架，配备两个统计学习组件：逆半监督学习——通过逆映射与残差去偏利用大量未配对的刺激嵌入；以及元迁移学习——通过稀疏聚合与残差校正从跨被试预训练模型中借力。两种方法均仅在对齐阶段运行，同时保持编码器与解码器冻结，从而支持高效计算、模块化部署及严密的理论分析。我们建立了有限样本泛化界与安全性保证，并在大规模fMRI-图像重建基准数据上展示了具有竞争力的实证性能。