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A probabilistic approach to demixing odors

Grabska-Barwińska, A; Barthelmé, S; Beck, J; Mainen, ZF; Pouget, A; Latham, PE; (2017) A probabilistic approach to demixing odors. Nature Neuroscience , 20 (1) pp. 98-106. 10.1038/nn.4444. Green open access

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Abstract

The olfactory system faces a hard problem: on the basis of noisy information from olfactory receptor neurons (the neurons that transduce chemicals to neural activity), it must figure out which odors are present in the world. Odors almost never occur in isolation, and different odors excite overlapping populations of olfactory receptor neurons, so the central challenge of the olfactory system is to demix its input. Because of noise and the large number of possible odors, demixing is fundamentally a probabilistic inference task. We propose that the early olfactory system uses approximate Bayesian inference to solve it. The computations involve a dynamical loop between the olfactory bulb and the piriform cortex, with cortex explaining incoming activity from the olfactory receptor neurons in terms of a mixture of odors. The model is compatible with known anatomy and physiology, including pattern decorrelation, and it performs better than other models at demixing odors.

Type: Article
Title: A probabilistic approach to demixing odors
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1038/nn.4444
Publisher version: http://dx.doi.org/10.1038/nn.4444
Language: English
Additional information: Copyright © 2017 Nature America, Inc., part of Springer Nature. All rights reserved.
Keywords: Olfactory bulb, Sensory processing
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
URI: https://discovery.ucl.ac.uk/id/eprint/1533122
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