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首页> 外文期刊>The Journal of Neuroscience: The Official Journal of the Society for Neuroscience >Predicting the Dynamics of Network Connectivity in the Neocortex
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Predicting the Dynamics of Network Connectivity in the Neocortex

机译:预测Neocortex中网络连接的动态

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Dynamic remodeling of connectivity is a fundamental feature of neocortical circuits. Unraveling the principles underlying these dynamics is essential for the understanding of how neuronal circuits give rise to computations. Moreover, as complete descriptions of the wiring diagram in cortical tissues are becoming available, deciphering the dynamic elements in these diagrams is crucial for relating them to cortical function. Here, we used chronic in vivo two-photon imaging to longitudinally follow a few thousand dendritic spines in the mouse auditory cortex to study the determinants of these spines' lifetimes. We applied nonlinear regression to quantify the independent contribution of spine age and several morphological parameters to the prediction of the future survival of a spine. Weshow that spine age, size, and geometry are parameters that can provide independent contributions to the prediction of the longevity of a synaptic connection. In addition, we use this framework to emulate a serial sectioning electron microscopy experiment and demonstrate how incorporation of morphological information of dendritic spines from a single time-point allows estimation of future connectivity states. The distinction between predictable and nonpredictable connectivity changes may be used in the future to identify the specific adaptations of neuronal circuits to environmental changes. The full dataset is publicly available for further analysis.
机译:连接性的动态重塑是新皮层回路的基本特征。弄清这些动力学的基本原理对于理解神经元回路如何引起计算至关重要。此外,随着对皮质组织中接线图的完整描述变得可用,解密这些图中的动态元素对于使它们与皮质功能相关至关重要。在这里,我们使用慢性体内双光子成像技术纵向跟踪小鼠听觉皮层中的数千个树突棘,以研究这些棘突寿命的决定因素。我们应用非线性回归来量化脊柱年龄和几个形态参数对脊柱未来生存的预测的独立贡献。我们显示,脊柱的年龄,大小和几何形状是可以为突触连接的寿命预测提供独立贡献的参数。此外,我们使用此框架来模拟连续切片电子显微镜实验,并演示如何从单个时间点合并树突棘的形态学信息可以估计未来的连接状态。可预测和不可预测的连接性变化之间的区别可能会在将来用于识别神经元回路对环境变化的特定适应性。完整的数据集可公开获得以供进一步分析。

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