Two simulations were developed: one quantifying the model’s inference ability on a timescale of hundreds of milliseconds, while the other examining how the required number of OSNs scales with the size of the potential odorant dictionary.

Next, we mapped the model’s inference dynamics on the circuit architecture of the olfactory bulb. Notably, SDEO naturally gives rise to two classes of projection neurons resembling mitral and tufted cells and providing experimentally testable predictions.

We then built a biologically plausible recurrent neural network implementing these sampling dynamics. Through simulations, we demonstrated that our SDEO accurately tracks the presence and concentration of changing odorants.

MLD performs sampling in an unconstrained dual space and projects samples back to the constrained primal space via an invertible mirror map, therefore obtaining samples from a constrained distribution.

We proposed “SDEO”, a model for olfactory compressed sensing inspired by simultaneous localization and mapping (SLAM) algorithms in navigation: the set of odors that are present in a given scene, and the concentration of those present odors, are inferred separately.

Two simulations were developed: one quantifying the model’s inference ability on a timescale of hundreds of milliseconds, while the other examining how the required number of OSNs scales with the size of the potential odorant dictionary.

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Next, we mapped the model’s inference dynamics on the circuit architecture of the olfactory bulb. Notably, SDEO naturally gives rise to two classes of projection neurons resembling mitral and tufted cells and providing experimentally testable predictions.

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We then built a biologically plausible recurrent neural network implementing these sampling dynamics. Through simulations, we demonstrated that our SDEO accurately tracks the presence and concentration of changing odorants.

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MLD performs sampling in an unconstrained dual space and projects samples back to the constrained primal space via an invertible mirror map, therefore obtaining samples from a constrained distribution.

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We proposed “SDEO”, a model for olfactory compressed sensing inspired by simultaneous localization and mapping (SLAM) algorithms in navigation: the set of odors that are present in a given scene, and the concentration of those present odors, are inferred separately.

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