# Reading out olfactory receptors: feedforward circuits detect odors in mixtures without demixing

## TL;DR

This study explores how the mouse brain detects specific odors in mixtures without separating them, using a model of neural responses.

## Contribution

The paper introduces a model showing that mice detect odors in mixtures using a sparse linear decoder without demixing.

## Key findings

- A linear decoder with sparse weights can match mouse performance using only ~15 glomeruli.
- Decoders trained on single odors generalize poorly to mixtures due to nonlinear responses.
- Mice fail to generalize without demixing, suggesting they use discriminative learning for odor detection.

## Abstract

The olfactory system, like other sensory systems, can detect specific stimuli of interest amidst complex, varying backgrounds. To gain insight into the neural mechanisms underlying this ability, we imaged responses of mouse olfactory bulb glomeruli to single odors and hundreds of mixtures. We used this data to build a model of mixture responses that incorporated nonlinear interactions and trial-to-trial variability and explored potential decoding mechanisms that can mimic mouse performance from our previous study (Rokni et al., 2014) when given glomerular responses as input. We find that a linear decoder with sparse weights could match mouse performance using just a small subset of the glomeruli (~ 15). However, when such a decoder is trained only with single odors, it generalizes poorly to mixture stimuli due to nonlinear mixture responses. We show that mice similarly fail to generalize, although they could in principle fully decompose the mixtures. This suggests that mice learn this segregation task discriminatively by adjusting task-specific decision boundaries without taking advantage of a demixed representation of odors.