Tootoonian Sina, True Aaron C, Stark Elle, Crimaldi John P, Schaefer Andreas T
Sensory Circuits and Neurotechnology Laboratory, The Francis Crick Institute, London, United Kingdom.
Department of Civil, Environmental and Architectural Engineering, University of Colorado, Boulder, CO, United States of America.
PLoS One. 2025 Jan 10;20(1):e0297754. doi: 10.1371/journal.pone.0297754. eCollection 2025.
Odours released by objects in natural environments can contain information about their spatial locations. In particular, the correlation of odour concentration timeseries produced by two spatially separated sources contains information about the distance between the sources. For example, mice are able to distinguish correlated and anti-correlated odour fluctuations at frequencies up to 40 Hz, while insect olfactory receptor neurons can resolve fluctuations exceeding 100 Hz. Can this high-frequency acuity support odour source localization? Here we answer this question by quantifying the spatial information about source separation contained in the spectral constituents of correlations. We used computational fluid dynamics simulations of multisource plumes in two-dimensional chaotic flow environments to generate temporally complex, covarying odour concentration fields. By relating the correlation of these fields to the spectral decompositions of the associated odour concentration timeseries, and making simplifying assumptions about the statistics of these decompositions, we derived analytic expressions for the Fisher information contained in the spectral components of the correlations about source separation. We computed the Fisher information for a broad range of frequencies and source separations for three different source arrangements and found that high frequencies were more informative than low frequencies when sources were close relative to the sizes of the large eddies in the flow. We observed a qualitatively similar effect in an independent set of simulations with different geometry, but not for surrogate data with a similar power spectrum to our simulations but in which all frequencies were a priori equally informative. Our work suggests that the high-frequency acuity of olfactory systems may support high-resolution spatial localization of odour sources. We also provide a model of the distribution of the spectral components of correlations that is accurate over a broad range of frequencies and source separations. More broadly, our work establishes an approach for the quantification of the spatial information in odour concentration timeseries.
自然环境中物体释放的气味可能包含有关其空间位置的信息。特别是，由两个空间分离的源产生的气味浓度时间序列的相关性包含有关源之间距离的信息。例如，小鼠能够区分频率高达40赫兹的相关和反相关气味波动，而昆虫嗅觉受体神经元能够分辨超过100赫兹的波动。这种高频敏锐度能否支持气味源定位？在这里，我们通过量化相关性频谱成分中包含的关于源分离的空间信息来回答这个问题。我们使用二维混沌流环境中多源羽流的计算流体动力学模拟来生成时间上复杂的、协变的气味浓度场。通过将这些场的相关性与相关气味浓度时间序列的频谱分解相关联，并对这些分解的统计特性做出简化假设，我们推导出了相关性频谱成分中关于源分离的费希尔信息的解析表达式。我们计算了三种不同源排列在广泛频率和源分离范围内的费希尔信息，发现当源相对于流中大涡旋的尺寸较近时，高频比低频提供的信息更多。在具有不同几何形状的另一组独立模拟中，我们观察到了定性相似的效果，但对于具有与我们的模拟相似功率谱但所有频率先验地具有同等信息性的替代数据则没有观察到。我们的工作表明，嗅觉系统的高频敏锐度可能支持气味源的高分辨率空间定位。我们还提供了一个相关性频谱成分分布的模型，该模型在广泛的频率和源分离范围内都是准确的。更广泛地说，我们的工作建立了一种量化气味浓度时间序列中空间信息的方法。
