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.

翻译：神经信息处理是完全经典的还是涉及量子力学要素，这仍是一个悬而未决的问题。本文提出了一种与模型无关、基于信息论的非经典性检验方法，该方法绕过了微观层面的假设，转而探究神经表示本身的结构。我们以自编码器作为一个透明的模型系统，在潜在空间中引入了一种贝尔型一致性检验，旨在探究在多种读出情境下获得的解码统计量是否能够由一个单一的正定潜在变量分布共同解释。通过将神经系统中类量子特征的探寻从微观动力学转向信息处理中可实验检验的约束，这项工作为探索神经计算的基础物理学开辟了一条新路径。