Learning to predict target location with turbulent odor plumes.
Learning to predict target location with turbulent odor plumes.
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DOI：
10.7554/elife.72196
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发表时间：
2022-08-12
期刊：
影响因子：
7.7
通讯作者：
Goldstein, Raymond E.
中科院分区：
文献类型：
--
作者：
Rigolli, Nicola;Magnoli, Nicodemo;Rosasco, Lorenzo;Seminara, Agnese;Goldstein, Raymond E.
动物行为和神经记录表明，大脑能够测量气味的强度和时间。然而，气味检测的强度或时间是否对嗅觉驱动的行为更有意义尚不清楚。为了解决这个问题，我们考虑利用目标释放的气味来定位目标的问题。我们要问的是，目标的位置是否最好通过测量其气味的时间与强度来预测，采样时间很短。为了回答这个问题，我们将气味传输的精确数值模拟数据输入机器学习算法，学习如何将气味与目标位置联系起来。我们发现，强度和时间可以分别预测目标位置，即使从几米的距离，但是，它们的功效随着空间中气味的稀释而变化。因此，使用来自不同范围的嗅觉的生物体可能必须在不同的模式之间切换。这对大脑在接近目标时应该如何表现气味有影响。我们展示了简单的策略，通过修改气味采样和适当地结合不同的措施在一起，以提高预测的准确性和鲁棒性。为了测试这些预测，当动物相对于目标移动时，或者在模拟浓缩与稀释环境的虚拟条件下，应该监测动物的行为和气味表现。
Animal behavior and neural recordings show that the brain is able to measure both the intensity and the timing of odor encounters. However, whether intensity or timing of odor detections is more informative for olfactory-driven behavior is not understood. To tackle this question, we consider the problem of locating a target using the odor it releases. We ask whether the position of a target is best predicted by measures of timing vs intensity of its odor, sampled for a short period of time. To answer this question, we feed data from accurate numerical simulations of odor transport to machine learning algorithms that learn how to connect odor to target location. We find that both intensity and timing can separately predict target location even from a distance of several meters; however, their efficacy varies with the dilution of the odor in space. Thus, organisms that use olfaction from different ranges may have to switch among different modalities. This has implications on how the brain should represent odors as the target is approached. We demonstrate simple strategies to improve accuracy and robustness of the prediction by modifying odor sampling and appropriately combining distinct measures together. To test the predictions, animal behavior and odor representation should be monitored as the animal moves relative to the target, or in virtual conditions that mimic concentrated vs dilute environments.