arXiv cs.AI· Yi Guo, Changhong Jing, Yong Hu, Yan Liu, Michael K. P. Ng, Shanshan Wang, Shuqiang Wang·· 9 天前AI 评分24
神经解码即认知推理：基于贝叶斯大脑理论提取元神经语义表征
Neural Decoding as Cognitive Inference
AI 导读
研究者将神经解码重构为受大脑内在先验约束的认知推理，提出生成高层"元神经语义表征"的方法，在五种神经记录模态和运动、感知、内部思维三个认知域的解码实验中，该方法重组了神经观测表征的几何结构，使元神经语义表征在不同认知任务间呈现一致的几何关系，并能从变化的神经活动中恢复稳定的认知状态。
正文 · 原文
Abstract:The brain maintains stable cognition despite continuously changing neural activity. How to extract stable cognitive states from variable neural observations remains a central problem in neural decoding. Existing neural decoding methods map neural observations to predefined external labels based on the stimulus-response principle, often capturing recording-specific spurious correlations. Inspired by how the brain infers the world, and specifically by Bayesian brain theory, we recast neural decoding as cognitive inference constrained by brain-intrinsic priors, yielding high-level meta-neural semantic representations. In decoding experiments spanning five neural recording modalities and three cognitive domains (motor, perception and internal mentation), our cognitive inference method reorganized the geometry of neural observation representations, yielding meta-neural semantic representations that exhibited consistent geometric relationships across cognitive tasks and enabled the recovery of stable cognitive states from variable neural observations. Our work provides an account of how the brain maintains relatively stable cognition despite continual changes in the external environment. Cognitive stability is sustained through cognitive inference from changing neural activity, without requiring fixed neural activity patterns.
| Subjects: | Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.11923 [q-bio.NC] |
| (or arXiv:2610.11923v1 [q-bio.NC] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11923 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shuqiang Wang [view email]
[v1]
Thu, 8 Oct 2026 13:19:57 UTC (42,691 KB)
来源：arXiv cs.AI · arxiv.org