用自编码器测试大脑信息处理是否含量子特征
Searching for Quantum Effects in the Brain: A Bell-Type Test for Nonclassical Latent Representations in Autoencoders
- 在隐空间设计类贝尔测试，检验潜在变量分布的非经典性
- 发现统计表示中存在违背经典一致性的现象
- 无需微观机制假设，适合研究神经计算基础物理
神经信息处理是否完全经典，或包含量子力学成分，仍是未解之谜。本文提出一种模型无关、基于信息论的非经典性检验方法，绕过微观假设，直接探测神经表征结构本身。以自编码器为透明模型系统，在隐空间引入类贝尔一致性检验，考察在多种读出情境下解码统计能否由单一正隐变量分布共同解释。该方法将寻找神经系统中量子似信号的焦点从微观动力学转向可实验验证的信息处理约束，开辟了探究神经计算基础物理的新路径。所提测试能在统计表征层面识别经典隐变量一致性的违反，无需假设特定物理机制。
原文摘要 · Abstract (English)
Whether neural information processing is entirely classical or involves quantum-mechanical elements remains an open question. Here we propose a model-agnostic, information-theoretic test of nonclassicality that bypasses microscopic assumptions and instead probes the structure of neural representations themselves. Using autoencoders as a transparent model system, we introduce a Bell-type consistency test in latent space, and ask whether decoding statistics obtained under multiple readout contexts can be jointly explained by a single positive latent-variable distribution. By shifting the search for quantum-like signatures in neural systems from microscopic dynamics to experimentally testable constraints on information processing, this work opens a new route for probing the fundamental physics of neural computation. The proposed test identifies violations of classical latent-variable consistency at the level of statistical representations, without assuming a specific underlying physical mechanism.
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