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 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 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, suggesting that they learn this segregation task discriminatively by adjusting task-specific decision boundaries without taking advantage of a demixed representation of odors.
Bibliographical noteFunding Information:
We thank Philipp Berens, Mackenzie Amoroso, Gonzalo Otazu, and Alexandra Ding for helpful feedback. This work was supported by Harvard University, by DFG grant MA 6176/1-1 (A.M.), and by a Marie Curie Fellowship PIOF-GA-2013-622943 (A.M.). M.B. has received financial support from the Bernstein Center for Computational Neuroscience (FKZ 01GQ1002) and the German Excellency Initiative through the Centre for Integrative Neuroscience Tubingen (EXC307). Research in V.N.M.’s lab is supported by grants from the NIH (DC011291, DC014453). Computational resources were provided and maintained by Harvard FAS Research Computing. We thank the Kavli Institute for Theoretical Physics for hosting the authors at a workshop supported in part by the National Science Foundation grant PHY11-25915.
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