[Submitted on 10 Aug 2026]
Title:Decoding Mixture Perception through Computational Modeling of Component Interactions
View a PDF of the paper titled Decoding Mixture Perception through Computational Modeling of Component Interactions, by Fei Wang and 8 other authors
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Abstract:Olfaction played an indispensable role throughout human evolution and civilization. Even in the contemporary era of advanced technology, olfaction remains a critical channel for person to conduct danger discrimination, emotional experience, and memory formation. However, most substances in nature exist as multi-molecule mixtures. The complexity of mixture compositions, as well as concentration dependent saturation effects and receptor specific activation thresholds, pose substantial challenges in identifying olfactory characteristics. In this study, we proposed a novel bio inspired deep learning framework for accurate odor perception recognition of mixtures. We robustly constructed neural response curves for molecule-receptor interactions, and developed a fusion strategy that integrates attention-weighted multi-receptor curves with concentration-dependent multi-molecule curves, replicating the competitive activation and synergistic integration of mixture components. Furthermore, by comparing the consistency of response curve patterns, the model can transfer knowledge from the semantically rich space of molecular associations to guide recognition of mixture perception characteristics. Therefore, we established a complete computational pathway from chemical blending, neural encoding, to perceptual formation. Finally, we conducted comprehensive evaluation, and results demonstrated exceptional superiority, achieving an accuracy of 92.2%. Consequently, our work provides a generalizable solution to the long standing mixture perception challenge. More importantly, it can be integrated into embodied cognitive systems to enhance the agents perceptual and interactive capabilities in complex scenarios.
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2609.11958 [cs.LG]
(or arXiv:2609.11958v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.11958
arXiv-issued DOI via DataCite
Submission history
From: Fei Wang [view email] [v1] Mon, 10 Aug 2026 15:13:19 UTC (2,687 KB)
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