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.
Enregistré dans:
Auteurs principaux: | Naoki Hiratani, Peter E. Latham |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2020
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/f6df5da2e76146c792e6313cc3b56b26 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Mixture interactions at mammalian olfactory receptors are dependent on the cellular environment
par: Elizabeth A. Corey, et autres
Publié: (2021) -
Rapid and continuous activity-dependent plasticity of olfactory sensory input
par: Claire E. J. Cheetham, et autres
Publié: (2016) -
Predictive olfactory learning in Drosophila
par: Chang Zhao, et autres
Publié: (2021) -
Rapid adaptation to microgravity in mammalian macrophage cells
par: Cora S. Thiel, et autres
Publié: (2017) -
Screening of COVID-19 cases through a Bayesian network symptoms model and psychophysical olfactory test
par: Susana Eyheramendy, et autres
Publié: (2021)