arXiv：cs.AI· Yi Guo, Changhong Jing, Yong Hu, Yan Liu, Michael K. P. Ng, Shanshan Wang, Shuqiang Wang·· 3 小时前
神经解码作为认知推理：受贝叶斯大脑理论启发的解码新方法
Neural Decoding as Cognitive Inference
AI 导读
研究者提出将神经解码重构为受大脑内在先验约束的认知推理，产出高层元神经语义表征。该方法在五种神经记录模态、三个认知领域（运动、感知与内部心理活动）的解码实验中重组了神经观测表征的几何结构，实现了跨认知任务一致的几何关系，并能从变化的神经活动中恢复稳定认知状态。
正文
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