@article {SI2019,
	title = {Structured Odorant Response Patterns across a Complete Olfactory Receptor Neuron Population},
	journal = {Neuron},
	volume = {101},
	number = {5},
	year = {2019},
	pages = {950-962},
	abstract = {Summary Odor perception allows animals to distinguish odors,\&nbsp;recognize the same odor across concentrations, and\&nbsp;determine concentration changes. How the activity patterns of primary olfactory receptor neurons (ORNs), at the individual and population levels, facilitate distinguishing these functions remains poorly understood. Here, we interrogate the complete ORN population of the Drosophila larva across a broadly sampled panel of odorants at varying concentrations. We find that the activity of each ORN scales with the concentration of any odorant via a\&nbsp;fixed dose-response function with a variable sensitivity. Sensitivities across odorants and ORNs follow a power-law distribution. Much of receptor sensitivity to odorants is accounted for by a single geometrical property of molecular structure. Similarity in the shape of temporal response filters across\&nbsp;odorants and ORNs extend these relationships to\&nbsp;fluctuating environments. These results uncover shared individual- and population-level patterns that together lend structure to support odor perceptions.},
	keywords = {calcium imaging, combinatorial olfactory code, dose response relationship, Microfluidics, molecular recognition, Olfactory Receptor Neurons, power lab distribution, receptor sensitivity, temporal filter},
	issn = {0896-6273},
	doi = {https://doi.org/10.1016/j.neuron.2018.12.030},
	url = {http://www.sciencedirect.com/science/article/pii/S0896627318311504},
	author = {Si, Guangwei and Jessleen K. Kanwal and Hu, Yu and Christopher J. Tabone and Baron, Jacob and Berck, Matthew and Vignoud, Gaetan and Aravinthan D. T. Samuel}
}
