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Updated: Jan 10, 2026

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Real-time In Vitro Monitoring of Odorant Receptor Activation by an Odorant in the Vapor Phase
Published on: April 23, 2019
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Simultaneous detection and estimation in olfactory sensing
Chen Jiang1,2, Matthew Y He1, Venkatesh N Murthy3,4,5
1Department of Psychology, McGill University, Montréal, QC, H3A 1G1, Canada.
Biorxiv : the Preprint Server for Biology
|November 24, 2025
Summary
This study introduces a novel olfactory compressed sensing model for accurately decoding odor identity and concentration. The new biologically-plausible recurrent circuit model handles complex naturalistic olfactory scenes effectively.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Sensory Systems
Background:
- Mammalian olfactory systems excel at rapid odor decoding.
- Compressed sensing theory explains odor decoding from limited receptors.
- Existing models struggle with complex, naturalistic olfactory scenes.
Purpose of the Study:
- To develop a new model for olfactory compressed sensing.
- To infer both odor presence and concentration separately in complex scenes.
- To create a biologically-plausible recurrent circuit for olfactory decoding.
Main Methods:
- Inspired by simultaneous localization and mapping (SLAM) algorithms.
- Utilized Mirrored Langevin Dynamics for rapid inference.
- Developed a rate-based dynamics framework for constrained distributions.
Main Results:
- The proposed model accurately infers odor presence and concentration at scale.
- The model is compatible with primary cell types in the olfactory bulb.
- Demonstrates a path towards advanced olfactory sensing models.
Conclusions:
- The new framework enables olfactory sensing in naturalistic environments.
- The model provides experimentally-testable predictions for neural dynamics.
- Offers a scalable and accurate approach to olfactory decoding.
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