Statistics, geometry & plasticity of cortical circuits

描述(由申请人提供)：了解大脑的发育和功能需要解开神经元回路中的突触连接。因为突触连接需要轴突和树突之间的物理接触，神经元的树枝形态可以用来推断连接。我们将开发统计理论工具，帮助从新的光学显微镜数据推断神经元连接。我们将重点解决以下问题，作为提案的具体目标：i)树突支和轴突分支的空间位置是相关的还是独立的？为了回答这个问题，我们将对神经元对的3D重建进行统计分析，II)潜在的突触连接如何取决于属于不同层的皮质神经元的相对位置？这个问题将通过分析来自不同皮质层的单个神经元的3D重建来解决，III)控制脊柱重建的动力学的规则是什么？我们将从分析转基因小鼠体内双光子成像中脊柱生长和回缩的实验数据开始。所有的理论工作都将与几个实验小组密切合作进行。

DESCRIPTION (provided by applicant): Understanding brain development and function requires unraveling synaptic connectivity in neuronal circuits. Because synaptic connections require physical contacts between axons and dendrites, neuronal arbor morphology may be used for inferring connectivity. We will develop statistical theoretical tools to help infer neuronal connectivity from new light microscopy data. We will focus on the following questions, serving as the specific aims of the proposal, i) Are spatial locations of dendritic and axonal branches correlated or independent? To answer this question we will perform statistical analysis of 3D reconstructions of neuronal pairs, ii) How does potential synaptic connectivity depend on the relative position of cortical neurons belonging to different layers? This question will be addressed by analyzing 3D reconstructions of single neurons from different cortical layers, iii) What rules govern the dynamics of spine remodeling? We will start by analyzing experimental data on spine growth and retraction from in vivo two-photon imaging of transgenic mice. All theoretical work will be carried out in close collaborations with several experimental groups.