Rapid Bayesian learning in the mammalian olfactory system
Rapid Bayesian learning in the mammalian olfactory system
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DOI：
10.1038/s41467-020-17490-0
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发表时间：
2020-07-31
影响因子：
16.6
通讯作者：
Latham, Peter E.
中科院分区：
文献类型：
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作者：
Hiratani, Naoki;Latham, Peter E.
许多实验研究表明，动物可以迅速学会识别气味并预测与之相关的奖励。然而，潜在的可塑性机制仍然难以捉摸。特别是，目前还不清楚嗅觉回路如何实现快速，数据有效的学习与局部突触可塑性。在这里，我们制定嗅觉学习贝叶斯优化过程，然后映射到哺乳动物嗅觉电路的计算模型的学习规则。该模型能够从少量的观察气味识别，同时再现细胞可塑性通常在发展过程中观察到的。我们将框架扩展到基于奖励的学习，并表明该电路能够快速学习气味奖励关联与合理的神经结构。这些结果加深了我们对哺乳动物大脑中无监督学习的理论理解。啮齿动物如何在没有监督的情况下感知嗅觉环境？在这里，作者将嗅觉学习公式化为一个集成的贝叶斯推理问题，然后推导出一组突触可塑性规则和神经动力学，使气味识别接近最佳学习。
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.