解码混合物感知：基于组分相互作用的计算建模
Decoding Mixture Perception through Computational Modeling of Component Interactions
- Zhejiang University（浙江大学）
- Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出仿生深度学习框架，通过融合注意力加权多受体与浓度依赖多分子响应曲线，建模组分相互作用，实现混合物气味感知识别，准确率达92.2%，可集成于具身系统。
AI中文摘要：
嗅觉在人类进化和文明进程中扮演着不可或缺的角色。即使在当代先进技术时代，嗅觉仍然是人类进行危险辨别、情感体验和记忆形成的关键通道。然而，自然界中大多数物质以多分子混合物的形式存在。混合物组成的复杂性，以及浓度依赖的饱和效应和受体特异性激活阈值，给嗅觉特征的识别带来了巨大挑战。在本研究中，我们提出了一种新颖的仿生深度学习框架，用于混合物气味的准确感知识别。我们稳健地构建了分子-受体相互作用的神经响应曲线，并开发了一种融合策略，该策略将注意力加权的多受体曲线与浓度依赖的多分子曲线相结合，重现了混合物组分的竞争性激活和协同整合。此外，通过比较响应曲线模式的一致性，模型可以从分子关联的语义丰富空间中迁移知识，以指导混合物感知特征的识别。因此，我们建立了一条从化学混合、神经编码到感知形成的完整计算通路。最后，我们进行了全面评估，结果表明其具有卓越的优越性，达到了92.2%的准确率。因此，我们的工作为长期存在的混合物感知挑战提供了一种通用解决方案。更重要的是，它可以集成到具身认知系统中，以增强智能体在复杂场景中的感知和交互能力。
英文摘要：
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.
