Optical reconstruction of cortical connectivity

皮质连接的光学重建

• 批准号: 0904353
• 负责人: Liam Paninski
• 金额: $ 73万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-10-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0904353&HistoricalAwards=false
• 关键词: Optical; reconstruction; cortical; connectivity

One of the greatest challenges in computational neuroscience is to reconstruct the connectivity of large, complex neuronal networks. The ability to decipher circuit connectivity would have a fundamental impact on our understanding of the dynamical properties and the functional organization of the nervous system. Knowledge of prevalent connectivity patterns will also shed light on the developmental constraints and learning rules under which the network might be operating. Recent developments open new possibilities for collaborative efforts to tackle this basic problem. First, advances in two-photon imaging and photostimulation methods make it possible to observe the simultaneous activity of large ensembles of neurons, while stimulating neurons in arbitrary spatiotemporal patterns. Second, new statistical methods for extracting action potential timing information from calcium imaging data, and for modeling the response properties of small collections of neurons, are now efficient enough that they may be implemented on-line and scaled up to understand the function of large networks. The investigators will combine these new experimental and analytical methods to estimate, for the first time, the connectivity diagram of large neocortical circuits, using two-photon calcium imaging of spontaneous and evoked activity in thalamocortical slices. A key novel step here is to directly verify the estimated circuit model with two-photon glutamate uncaging, which allows any neuron in the circuit to be activated (with single-cell resolution) while the evoked postsynaptic responses are monitored. This interdisciplinary project has three complementary specific aims: (1) Develop statistically-optimal methods for real-time inference of spike timing from calcium imaging data. (2) Use these spike timing inference methods to estimate the network connectivity from large-scale multineuronal calcium-imaging of cortical slices. (3) Confirm the derived connectivity maps with glutamate uncaging and patch clamping, by photoactivating putative presynaptic neurons while recording intracellularly from postsynaptic cells. The proposed methods should also prove applicable to study other central and peripheral regions of the nervous system; data analysis software will be made publicly available online, to enhance the infrastructure for research and education in computational neuroscience.

计算神经科学中最大的挑战之一是重建大型复杂神经元网络的连通性。破译电路连接的能力将对我们理解神经系统的动力学特性和功能组织产生根本性的影响。对普遍连通模式的了解还将有助于阐明该网络可能在其下运行的发展制约因素和学习规则。最近的事态发展为合作努力解决这一基本问题提供了新的可能性。首先，双光子成像和光刺激方法的进步使人们有可能观察到大量神经元的同时活动，同时以任意的时空模式刺激神经元。其次，从钙成像数据中提取动作电位计时信息，并对小部分神经元的反应特性进行建模的新统计方法，现在已经足够有效，可以在线实施并扩大规模，以了解大型网络的功能。研究人员将首次将这些新的实验和分析方法结合起来，使用双光子钙成像技术对丘脑皮质脑片的自发和诱发活动进行评估，以估计大型新皮质回路的连接图。这里的一个关键的新步骤是用双光子谷氨酸去离子化直接验证估计的电路模型，这允许电路中的任何神经元被激活(以单细胞分辨率)，同时监测诱发的突触后反应。这个跨学科的项目有三个相辅相成的具体目标：(1)开发统计最优的方法，从钙成像数据中实时推断尖峰时间。(2)使用这些棘波时序推断方法，从皮层脑片的大规模多神经元钙成像中估计网络的连通性。(3)用谷氨酸去化和膜片钳技术，在细胞内记录突触后神经元的同时，光激活可能的突触前神经元，以证实所获得的连接图。拟议的方法也应被证明适用于研究神经系统的其他中央和外围区域；数据分析软件将在网上公开提供，以加强计算神经科学研究和教育的基础设施。