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This study proposes a novel, model-agnostic framework using autoencoders and Bell-type tests to detect nonclassical latent representations in neural systems without relying on microscopic physical assumptions. By shifting the inquiry from dynamic mechanisms to statistical constraints on information processing, it establishes an experimentally testable route for probing whether quantum mechanics plays a fundamental role in biological computation. The work bridges theoretical physics and machine learning by defining specific consistency violations that would indicate genuine nonclassicality within neural architectures.
Searching for Quantum Effects in the Brain: A Bell-Type Test for Nonclassical Latent Representations in Autoencoders
arXiv:2601.10588v2 Announce Type: replace Abstract: 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.
Abstract: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.
Quantum Physics (quant-ph); Machine Learning (cs.LG); Biological Physics (physics.bio-ph)
arXiv:2601.10588 [quant-ph]
(or arXiv:2601.10588v2 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2601.10588 Focus to learn more arXiv-issued DOI via DataCite
New J. Phys. 28, 074511 (2026)
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