Rapid Bayesian learning in the mammalian olfactory system
How can rodents make sense of the olfactory environment without supervision? Here, the authors formulate olfactory learning as an integrated Bayesian inference problem, then derive a set of synaptic plasticity rules and neural dynamics that enables near-optimal learning of odor identification.
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| Format: | Article |
| Language: | English |
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Nature Publishing Group
2020-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-17490-0 |
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Article |
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doaj-f6df5da2e76146c792e6313cc3b56b262021-08-01T11:39:19ZengNature Publishing GroupNature Communications2041-17232020-07-0111111510.1038/s41467-020-17490-0Rapid Bayesian learning in the mammalian olfactory systemNaoki Hiratani0Peter E. Latham1Gatsby Computational Neuroscience Unit, University College LondonGatsby Computational Neuroscience Unit, University College LondonHow can rodents make sense of the olfactory environment without supervision? Here, the authors formulate olfactory learning as an integrated Bayesian inference problem, then derive a set of synaptic plasticity rules and neural dynamics that enables near-optimal learning of odor identification.https://doi.org/10.1038/s41467-020-17490-0 |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Naoki Hiratani Peter E. Latham |
| spellingShingle |
Naoki Hiratani Peter E. Latham Rapid Bayesian learning in the mammalian olfactory system Nature Communications |
| author_facet |
Naoki Hiratani Peter E. Latham |
| author_sort |
Naoki Hiratani |
| title |
Rapid Bayesian learning in the mammalian olfactory system |
| title_short |
Rapid Bayesian learning in the mammalian olfactory system |
| title_full |
Rapid Bayesian learning in the mammalian olfactory system |
| title_fullStr |
Rapid Bayesian learning in the mammalian olfactory system |
| title_full_unstemmed |
Rapid Bayesian learning in the mammalian olfactory system |
| title_sort |
rapid bayesian learning in the mammalian olfactory system |
| publisher |
Nature Publishing Group |
| series |
Nature Communications |
| issn |
2041-1723 |
| publishDate |
2020-07-01 |
| description |
How can rodents make sense of the olfactory environment without supervision? Here, the authors formulate olfactory learning as an integrated Bayesian inference problem, then derive a set of synaptic plasticity rules and neural dynamics that enables near-optimal learning of odor identification. |
| url |
https://doi.org/10.1038/s41467-020-17490-0 |
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AT naokihiratani rapidbayesianlearninginthemammalianolfactorysystem AT peterelatham rapidbayesianlearninginthemammalianolfactorysystem |
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