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.
