Joshi Shruti, Haney Seth, Wang Zhenyu, Locatelli Fernando, Lei Hong, Cao Yu, Smith Brian, Bazhenov Maxim
Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
Department of Medicine, University of California San Diego, La Jolla, CA, USA.
Commun Biol. 2025 Apr 9;8(1):590. doi: 10.1038/s42003-025-07879-2.
Distinguishing between nectar and non-nectar odors is challenging for animals due to shared compounds and varying ratios in complex mixtures. Changes in nectar production throughout the day and over the animal's lifetime add to the complexity. The honeybee olfactory system, containing fewer than 1000 principal neurons in the early olfactory relay, the antennal lobe (AL), must learn to associate diverse volatile blends with rewards. Previous studies identified plasticity in the AL circuits, but its role in odor learning remains poorly understood. Using a biophysical computational model, tuned by in vivo electrophysiological data, and live imaging of the honeybee's AL, we explored the neural mechanisms of plasticity in the AL. Our findings revealed that when trained with a set of rewarded and unrewarded odors, the AL inhibitory network suppresses responses to shared chemical compounds while enhancing responses to distinct compounds. This results in improved pattern separation and a more concise neural code. Our calcium imaging data support these predictions. Analysis of a graph convolutional neural network performing an odor categorization task revealed a similar mechanism for contrast enhancement. Our study provides insights into how inhibitory plasticity in the early olfactory network reshapes the coding for efficient learning of complex odors.
对于动物来说，区分花蜜气味和非花蜜气味具有挑战性，因为复杂混合物中存在共同的化合物且比例各异。花蜜产量在一天中以及动物一生中的变化增加了这种复杂性。蜜蜂的嗅觉系统在早期嗅觉中继——触角叶（AL）中包含不到1000个主要神经元，它必须学会将不同的挥发性混合物与奖励联系起来。先前的研究确定了AL回路中的可塑性，但其在气味学习中的作用仍知之甚少。我们使用一个由体内电生理数据调整的生物物理计算模型以及对蜜蜂AL的实时成像，探索了AL中可塑性的神经机制。我们的研究结果表明，当用一组有奖励和无奖励的气味进行训练时，AL抑制网络会抑制对共同化学化合物的反应，同时增强对不同化合物的反应。这导致模式分离得到改善，神经编码更加简洁。我们的钙成像数据支持了这些预测。对执行气味分类任务的图卷积神经网络的分析揭示了一种类似的对比度增强机制。我们的研究深入了解了早期嗅觉网络中的抑制性可塑性如何重塑编码，以实现对复杂气味的高效学习。
