ArticleNature communications2020
Rapid Bayesian learning in the mammalian olfactory system.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- How the olfactory bulb maintains stable odor manifolds amid adaptation and representational drift.bioRxiv : the preprint server for biology · 2026Article
- Simultaneous detection and estimation in olfactory sensing.bioRxiv : the preprint server for biology · 2025Article
- Predictive learning rules generate a cortical-like replay of probabilistic sensory experiences.eLife · 2025Article
- Expectation-driven sensory adaptations support enhanced acuity during categorical perception.Nature neuroscience · 2025Article
- Odor modality is transmitted to cortical brain regions from the olfactory bulb.Journal of neurophysiology · 2023Article
- Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb.bioRxiv : the preprint server for biology · 2023Article
- Cortical field maps across human sensory cortex.Frontiers in computational neuroscience · 2023Review
- Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb.Advances in neural information processing systems · 2023Article
- A new conceptual and quantitative approach to exploring and defining potential open-access olfactory information.The New phytologist · 2022Article
- Article
- The Olfactory Landscape Concept: A Key Source of Past, Present, and Future Information Driving Animal Movement and Decision-making.Bioscience · 2022Article
- Developmental and evolutionary constraints on olfactory circuit selection.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Disorder and the Neural Representation of Complex Odors.Frontiers in computational neuroscience · 2022Article
- Sparse connectivity for MAP inference in linear models using sister mitral cells.PLoS computational biology · 2022Article
- Article
- Learning improves decoding of odor identity with phase-referenced oscillations in the olfactory bulb.eLife · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Many experimental studies suggest that animals can rapidly learn to identify odors and predict the rewards associated with them. However, the underlying plasticity mechanism remains elusive. In particular, it is not clear how olfactory circuits achieve rapid, data efficient learning with local synaptic plasticity. Here, we formulate olfactory learning as a Bayesian optimization process, then map the learning rules into a computational model of the mammalian olfactory circuit. The model is capable of odor identification from a small number of observations, while reproducing cellular plasticity commonly observed during development. We extend the framework to reward-based learning, and show that the circuit is able to rapidly learn odor-reward association with a plausible neural architecture. These results deepen our theoretical understanding of unsupervised learning in the mammalian brain.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.