基于统计学习的潜在嵌入对齐及其在脑编码与解码中的应用
Statistical Learning for Latent Embedding Alignment with Application to Brain Encoding and Decoding
AI总结：
本文提出轻量级对齐框架，通过逆半监督学习和元转移学习提升有限fMRI-刺激配对数据下的样本效率，实现脑编码解码的高效计算与理论分析。
摘要：
脑编码与解码旨在理解外部刺激与脑活动之间的关系，是神经科学中的基本问题。本文研究了用于脑编码与解码的潜在嵌入对齐，重点在于在有限的fMRI-刺激配对数据和显著的受试者异质性下提高样本效率。我们提出了一种轻量级对齐框架，配备了两个统计学习组件：逆半监督学习利用大量的无配对刺激嵌入通过逆映射和残差去偏，以及元转移学习通过稀疏聚合和残差校正从预训练模型中借用强度。这两种方法仅在对齐阶段运行，同时保持编码器和解码器冻结，从而实现高效的计算、模块化的部署和严格的理论分析。我们建立了有限样本的泛化界限和安全保证，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。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
