嗅球模型中神经发生驱动刺激去相关
Neurogenesis Drives Stimulus Decorrelation in a Model of the Olfactory Bulb
摘要：
神经元网络对相似活动模式的重塑和去相关可以增强它们的可辨别性、存储和检索。当嗅觉系统中出现新的复杂模式时，这些网络如何学会去相关？使用计算网络模型的优势神经元群体的嗅球，我们表明，基本方面的成年神经发生观察到的嗅球-持续增加新的抑制颗粒细胞的网络，他们的活动依赖的生存，以及它们与主僧帽细胞的突触的相互特征--足以重构网络，并自适应地改变其气味刺激的编码，以减少相似刺激的延髓表示之间的相关性。的去相关是相当强大的各种类型的扰动的互易性。该模型简洁地捕捉到了实验观察到的神经发生在感知学习中的作用，以及年轻颗粒细胞对新刺激的反应增强。此外，它作出了具体的预测的类型的气味富集，应有效地提高动物的能力，以区分类似的气味混合物。
英文摘要：
The reshaping and decorrelation of similar activity patterns by neuronal networks can enhance their discriminability, storage, and retrieval. How can such networks learn to decorrelate new complex patterns, as they arise in the olfactory system? Using a computational network model for the dominant neural populations of the olfactory bulb we show that fundamental aspects of the adult neurogenesis observed in the olfactory bulb -- the persistent addition of new inhibitory granule cells to the network, their activity-dependent survival, and the reciprocal character of their synapses with the principal mitral cells -- are sufficient to restructure the network and to alter its encoding of odor stimuli adaptively so as to reduce the correlations between the bulbar representations of similar stimuli. The decorrelation is quite robust with respect to various types of perturbations of the reciprocity. The model parsimoniously captures the experimentally observed role of neurogenesis in perceptual learning and the enhanced response of young granule cells to novel stimuli. Moreover, it makes specific predictions for the type of odor enrichment that should be effective in enhancing the ability of animals to discriminate similar odor mixtures.
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