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.
journal_name
Nat Neuroscijournal_title
Nature neuroscienceauthors
Grabska-Barwińska A,Barthelmé S,Beck J,Mainen ZF,Pouget A,Latham PEdoi
10.1038/nn.4444subject
Has Abstractpub_date
2017-01-01 00:00:00pages
98-106issue
1eissn
1097-6256issn
1546-1726pii
nn.4444journal_volume
20pub_type
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