The morpho-functional properties of neural networks constantly adapt in response to environmental stimuli. The olfactory bulb is particularly prone to constant reshaping of neural networks because of ongoing neurogenesis. It remains unclear whether the complexity of distinct odor-induced learning paradigms and sensory stimulation induces different forms of structural plasticity. In the present study, we automatically reconstructed spines in 3D from confocal images and performed unsupervised clustering based on morphometric features. We show that while sensory deprivation decreased the spine density of adult-born neurons without affecting the morphometric properties of these spines, simple and complex odor learning paradigms triggered distinct forms of structural plasticity. A simple odor learning task affected the morphometric properties of the spines, whereas a complex odor learning task induced changes in spine density. Our work reveals distinct forms of structural plasticity in the olfactory bulb tailored to the complexity of odor-learning paradigms and sensory inputs.

神经网络的形态功能特性不断适应环境刺激。由于持续的神经发生，嗅球特别容易不断重塑神经网络。目前尚不清楚不同气味诱发的学习范式和感官刺激的复杂性是否会诱发不同形式的结构可塑性。在本研究中，我们从共焦图像自动重建 3D 脊柱，并根据形态特征进行无监督聚类。我们发现，虽然感觉剥夺降低了成年神经元的棘密度，但不影响这些棘的形态测量特性，但简​​单和复杂的气味学习范式引发了不同形式的结构可塑性。简单的气味学习任务会影响脊柱的形态特性，而复杂的气味学习任务会引起脊柱密度的变化。我们的工作揭示了嗅球中不同形式的结构可塑性，这些结构可塑性是根据气味学习范式和感官输入的复杂性而定制的。