发表机构
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences; The Hong Kong Polytechnic University; Hong Kong Baptist University（中国科学院深圳先进技术研究院; 中国科学院大学; 香港理工大学; 香港浸会大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将神经解码重新表述为受大脑固有先验约束的认知推理方法，在多模态神经记录的多认知领域实验中，成功从可变神经观测中恢复稳定认知状态，揭示了大脑维持认知稳定性的机制。
AI 中文摘要
尽管神经活动持续变化，大脑仍能维持稳定的认知状态。如何从可变的神经观测中提取稳定的认知状态，是神经解码领域的核心问题。现有神经解码方法基于刺激-响应原理将神经观测映射到预定义的外部标签，往往会捕获记录特有的虚假相关性。受大脑推断世界的方式（尤其是贝叶斯大脑理论）启发，我们将神经解码重新表述为受大脑固有先验约束的认知推理，从而产生高级元神经语义表征。在涵盖五种神经记录模态和三个认知领域（运动、感知和内部思维）的解码实验中，我们的认知推理方法重组了神经观测表征的几何结构，产生的元神经语义表征在认知任务间表现出一致的几何关系，能够从可变的神经观测中恢复稳定的认知状态。本研究揭示了大脑如何在外部环境持续变化的情况下维持相对稳定的认知：认知稳定性通过从变化的神经活动中进行认知推理来维持，无需固定的神经活动模式。
英文摘要
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.
