Ideas Lab Collaborative Research: Using Natural Odor Stimuli to Crack the Olfactory Code

该项目是在NSF关于“破解嗅觉密码”的想法实验室期间开发的，并由化学部的生命过程化学计划，数学科学部的数学生物学计划，物理部的生命系统物理计划，综合有机系统部的神经系统集群，生物基础设施部，和新兴边境司嗅觉对于维持人类的生活质量至关重要，它的下降可能是神经退行性疾病的重要预兆。此外，由于除了灵长类动物之外，几乎所有动物的大多数生存功能都依赖嗅觉，因此了解化学传感具有巨大的实用价值，例如，在控制农业害虫或训练动物检测与炸弹，药物和癌症检测相关的气味方面。尽管嗅觉很重要，但对嗅觉的理解远远落后于其他感官，部分原因是缺乏对气味物理空间的理解。通过对视觉和听觉刺激的物理维度的研究，对视觉和听觉的神经基础的理解得到了极大的推进。因此，对气味空间进行类似的深入研究--自然气味是如何产生的，以及它们必须被检测到的背景--很可能会揭示大脑中气味神经表征的新深度。果蝇和蜜蜂等昆虫是这项研究的极好模型，因为它们的中枢神经系统易于接近，因为它们在受控的实验室条件下易于使用，并且因为昆虫和哺乳动物大脑中处理气味的功能相似。这项研究将描述气味花和水果的行为价值蜜蜂（食物）和果蝇（食物和产卵地点）。当与计算建模相结合时，对大脑中早期和后期处理中的神经活动的进一步监测将揭示比迄今为止所描述的更丰富的神经表征。这一新的理解将对理解健康的大脑如何编码气味的感觉和记忆以及大脑如何在疾病条件下失败产生影响。这也将对理解嗅觉如何被内置到工程设备中产生影响。最后，这两种昆虫对农作物授粉（蜜蜂）和水果损害（果蝇）也具有重要的经济意义。PI将与本科生，研究生和博士后学生一起教学和工作，特别是从科学领域代表性不足的群体中招收学生。这项研究将定量描述多组分自然气味场景的真实统计数据，并研究它们如何驱动几个大脑区域的行为和处理。重点将是蜜蜂以及果蝇成虫和幼虫作为模型，在那里将有可能表征与行为输出的多样性相关的行为学相关的自然气味库。这项工作将开始定量表征的详细统计特性的自然气味场景中定义的行为学背景。这将建立在昆虫和哺乳动物中识别天然气味的丰富文献基础上。从每种昆虫的自然环境中收集自然产生的植物和水果气味样本，并进行化学分析。基于稀疏编码的非线性降维技术和方法将确定气味空间的维度，这些维度对于行为决策来说是最突出的。这种对感觉输入的定量解构在嗅觉神经科学中是前所未有的，并且应该允许PI第一次有效地和全面地驱动嗅觉回路。该假设是，与动物行为最相关的刺激维度将被嗅觉系统最有效地提取。合成气味混合物将被特别构造成沿着相关的感觉维度变化沿着，以探测嗅觉系统中的神经代码和适应行为。在视觉系统的研究中，使用考虑到自然气味统计的统计方法来分析这种诱发的神经反应，将揭示以前无法获得的新颖的嗅觉计算和行为。该项目将生成对理论生物学和数学，工程和生物学科学家立即使用和重要的数据集。

This project was developed during a NSF Ideas Lab on "Cracking the Olfactory Code" and is jointly funded by the Chemistry of Life Processes program in the Chemistry Division, the Mathematical Biology program in the Division of Mathematical Sciences, the Physics of Living Systems program in the Physics Division, the Neural Systems Cluster in the Division of Integrative Organismal Systems, the Division of Biological Infrastructure, and the Division of Emerging Frontiers. The sense of smell is essential for maintaining quality of life in humans, and its decline can be an important harbinger of neurodegenerative disease. Moreover, since nearly all animals aside from primates rely on olfaction for most survival functions, understanding chemical sensing has immense practical value, for example, in the control of agricultural pests or in training animals to detect odors relevant for bomb, drug and cancer detection. In spite of its importance, the understanding of olfaction lags far behind the other senses, which is in part due to the lack of understanding of the physical space of odors. The understanding of the neural bases of vision and audition were greatly advanced by investigations of the physical dimensions of visual and auditory stimuli. It is therefore likely that a similar in-depth investigation of odor space - how natural odors occur and the backgrounds against which they must be detected - will reveal a new depth of richness of neural representations of odors in the brain. Insects such as the fruit fly and honey bee are excellent models for this research because of the accessibility of their central nervous systems, because of their ease of use under controlled laboratory conditions, and because of the functional similarity of how odors are processed in insect and mammalian brains. This research will characterize how odor flowers and fruits with respect to behavioral value for honey bees (food) and fruit flies (food and egg laying sites). Further monitoring of neural activity in early and later stage processing in the brain, when combined with computational modeling, will reveal significantly richer neural representations than have heretofore been described. This new understanding stands to have an impact on understanding how healthy brains encode sensations and memories of odors and how brains fail under disease conditions. It will also have an impact on understanding how the sense of smell may be built into engineered devices. Finally, both insects are also of economic importance to agriculture for crop pollination (honey bees) and damage to fruit (fruit flies). The PIs will teach and work with undergraduate, graduate and postdoctoral students and especially recruit students from underrepresented groups in science. This research will quantitatively characterize the real-world statistics of multi-component natural odor scenes and investigate how they drive behavior and processing in several brain regions. The focus will be on honey bee as well as fruit fly adults and larva as models, where it will be possible to characterize a library of ethologically relevant natural odors associated with a diversity of behavioral outputs. The work will begin by quantitatively characterizing the detailed statistical properties of natural odor scenes in defined ethological contexts. This will build on the rich literature on identified natural odors in insects and mammals. Naturally occurring plant and fruit odor samples from the natural environments of each insect will be collected and chemically analyzed. Nonlinear dimensionality reduction techniques and approaches based on sparse coding will determine the dimensions of odor space that are most salient for behavioral decisions. Such a quantitative deconstruction of the sensory input would be unprecedented in olfactory neuroscience, and should allow the PIs to effectively and comprehensively drive olfactory circuits for the first time. The hypothesis is that the stimulus dimensions that are most behaviorally relevant to the animal will be most efficiently extracted by the olfactory system. Synthetic odor blends will be specially constructed to vary along relevant sensory dimensions, to probe neural codes and adaptive behaviors in the olfactory system. As in research on the visual system, analysis of such evoked neural responses using statistical methods that take into account natural odor statistics will reveal novel olfactory computations and behaviors that have been previously inaccessible. The project will generate datasets of immediate use and importance to scientists in theoretical biology and mathematics, engineering and biology.