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.
Saved in:
| Main Authors: | , |
|---|---|
| Format: | article |
| Language: | EN |
| Published: |
Nature Portfolio
2020
|
| Subjects: | |
| Online Access: | https://doaj.org/article/f6df5da2e76146c792e6313cc3b56b26 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|