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.
Highlights
Many experimental studies suggest that animals can rapidly learn to identify odors and predict the rewards associated with them
Our model still leads to faster learning of olfactory stimuli compared to previously proposed sparse-coding-based approaches[10,11,12]. It provides some insight into olfactory circuitry: it reveals the advantage, relative to the rectified linear transfer function[13], of sigmoidal-shaped f–I curves typical of biological neurons[14,15], and it reproduces the reduction in neuronal input gain[16,17] and learning rate[18] commonly observed during development
We extended our model to an odor–reward association task, and found that learning of a concentration invariant representation at the piriform cortex helps rapid odor–reward association
Summary
Many experimental studies suggest that animals can rapidly learn to identify odors and predict the rewards associated with them. Our model still leads to faster learning of olfactory stimuli compared to previously proposed sparse-coding-based approaches[10,11,12] It provides some insight into olfactory circuitry: it reveals the advantage, relative to the rectified linear transfer function[13], of sigmoidal-shaped f–I curves typical of biological neurons[14,15], and it reproduces the reduction in neuronal input gain[16,17] and learning rate[18] commonly observed during development. We extended our model to an odor–reward association task, and found that learning of a concentration invariant representation at the piriform cortex helps rapid odor–reward association
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