Sussex Neuroscience, School of Engineering and Informatics, University of Sussex, Falmer, Brighton, United Kingdom.
Department of Neuroscience, University of Konstanz, Konstanz, Germany.
PLoS Comput Biol. 2018 Dec 10;14(12):e1006536. doi: 10.1371/journal.pcbi.1006536. eCollection 2018 Dec.
In natural environments, odors are typically mixtures of several different chemical compounds. However, the implications of mixtures for odor processing have not been fully investigated. We have extended a standard olfactory receptor model to mixtures and found through its mathematical analysis that odorant-evoked activity patterns are more stable across concentrations and first-spike latencies of receptor neurons are shorter for mixtures than for pure odorants. Shorter first-spike latencies arise from the nonlinear dependence of binding rate on odorant concentration, commonly described by the Hill coefficient, while the more stable activity patterns result from the competition between different ligands for receptor sites. These results are consistent with observations from numerical simulations and physiological recordings in the olfactory system of insects. Our results suggest that mixtures allow faster and more reliable olfactory coding, which could be one of the reasons why animals often use mixtures in chemical signaling.
在自然环境中，气味通常是几种不同化学化合物的混合物。然而，混合物对气味处理的影响尚未得到充分研究。我们已经将标准嗅觉受体模型扩展到混合物，并通过其数学分析发现，与纯气味剂相比，受体神经元的浓度和第一峰潜伏期的气味剂诱发的活动模式更稳定。较短的第一峰潜伏期源于结合速率对气味浓度的非线性依赖性，通常由 Hill 系数描述，而更稳定的活动模式则源于不同配体对受体部位的竞争。这些结果与昆虫嗅觉系统的数值模拟和生理记录的观察结果一致。我们的结果表明，混合物允许更快和更可靠的嗅觉编码，这可能是动物在化学信号中经常使用混合物的原因之一。
