Rapid Bayesian learning in the mammalian olfactory system

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. 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.