Locatelli Fernando F, Fernandez Patricia C, Smith Brian H
School of Life Sciences, PO Box 874501, Arizona State University, Tempe, AZ 85287, USA.
School of Life Sciences, PO Box 874501, Arizona State University, Tempe, AZ 85287, USA
J Exp Biol. 2016 Sep 1;219(Pt 17):2752-62. doi: 10.1242/jeb.141465. Epub 2016 Jul 13.
Natural odors are typically mixtures of several chemical components. Mixtures vary in composition among odor objects that have the same meaning. Therefore a central 'categorization' problem for an animal as it makes decisions about odors in natural contexts is to correctly identify odor variants that have the same meaning and avoid variants that have a different meaning. We propose that identified mechanisms of associative and non-associative plasticity in early sensory processing in the insect antennal lobe and mammalian olfactory bulb are central to solving this problem. Accordingly, this plasticity should work to improve categorization of odors that have the opposite meanings in relation to important events. Using synthetic mixtures designed to mimic natural odor variation among flowers, we studied how honey bees learn about and generalize among floral odors associated with food. We behaviorally conditioned honey bees on a difficult odor discrimination problem using synthetic mixtures that mimic natural variation among snapdragon flowers. We then used calcium imaging to measure responses of projection neurons of the antennal lobe, which is the first synaptic relay of olfactory sensory information in the brain, to study how ensembles of projection neurons change as a result of behavioral conditioning. We show how these ensembles become 'tuned' through plasticity to improve categorization of odors that have the different meanings. We argue that this tuning allows more efficient use of the immense coding space of the antennal lobe and olfactory bulb to solve the categorization problem. Our data point to the need for a better understanding of the 'statistics' of the odor space.
自然气味通常是几种化学成分的混合物。在具有相同意义的气味对象中，混合物的成分各不相同。因此，动物在自然环境中对气味做出决策时面临的一个核心“分类”问题是，正确识别具有相同意义的气味变体，并避免具有不同意义的变体。我们提出，昆虫触角叶和哺乳动物嗅球早期感觉处理中已确定的联想和非联想可塑性机制是解决这个问题的核心。因此，这种可塑性应有助于改善与重要事件相关的具有相反意义的气味的分类。我们使用旨在模拟花朵间自然气味变化的合成混合物，研究了蜜蜂如何学习并归纳与食物相关的花香气味。我们通过使用模拟金鱼草花朵间自然变化的合成混合物，让蜜蜂在一个困难的气味辨别问题上进行行为条件训练。然后，我们使用钙成像技术来测量触角叶投射神经元的反应，触角叶是大脑中嗅觉感觉信息的第一个突触中继站，以此研究投射神经元群体如何因行为条件训练而发生变化。我们展示了这些群体如何通过可塑性“调整”，以改善对具有不同意义的气味的分类。我们认为，这种调整能够更有效地利用触角叶和嗅球巨大的编码空间来解决分类问题。我们的数据表明，有必要更好地理解气味空间的“统计学”特征。
