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The optimal odor-receptor interaction network is sparse in olfactory systems: Compressed sensing by nonlinear neurons with a finite dynamic range

View ORCID ProfileShanshan Qin, Qianyi Li, Chao Tang, Yuhai Tu
doi: https://doi.org/10.1101/464875
Shanshan Qin
1Center for Quantitative Biology, Peking University, Beijing, 100871, China
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Qianyi Li
2Yuanpei College, Peking University, Beijing, 100871, China
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Chao Tang
1Center for Quantitative Biology, Peking University, Beijing, 100871, China
3School of Physics, Peking University, Beijing 100871, China
4Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
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  • For correspondence: tangc@pku.edu.cn
Yuhai Tu
5IBM T. J. Watson Research Center, Yorktown Heights, New York 10598, USA
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  • For correspondence: yuhai@us.ibm.com
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Abstract

There are numerous different odorant molecules in nature but only a relatively small number of olfactory receptor neurons (ORNs) in brains. This “compressed sensing” challenge is compounded by the constraint that ORNs are nonlinear sensors with a finite dynamic range. Here, we investigate possible optimal olfactory coding strategies by maximizing mutual information between odor mixtures and ORNs’ responses with respect to the bipartite odor-receptor interaction network (ORIN) characterized by sensitivities between all odorant-ORN pairs. We find that the optimal ORIN is sparse – a finite fraction of sensitives are zero, and the nonzero sensitivities follow a broad distribution that depends on the odor statistics. We show that the optimal ORIN enhances performances of downstream learning tasks (reconstruction and classification). For ORNs with a finite basal activity, we find that having a basal-activity-dependent fraction of inhibitory odor-receptor interactions increases the coding capacity. All our theoretical findings are consistent with existing experiments and predictions are made to further test our theory. The optimal coding model provides a unifying framework to understand the peripheral olfactory systems across different organisms.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 10, 2019.
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The optimal odor-receptor interaction network is sparse in olfactory systems: Compressed sensing by nonlinear neurons with a finite dynamic range
Shanshan Qin, Qianyi Li, Chao Tang, Yuhai Tu
bioRxiv 464875; doi: https://doi.org/10.1101/464875
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The optimal odor-receptor interaction network is sparse in olfactory systems: Compressed sensing by nonlinear neurons with a finite dynamic range
Shanshan Qin, Qianyi Li, Chao Tang, Yuhai Tu
bioRxiv 464875; doi: https://doi.org/10.1101/464875

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