CRCNS: Functional Imaging and Computational Models of Place Field Integration in Pyramidal Cell Dendrites

CRCNS：锥体细胞树突中位置场整合的功能成像和计算模型

• 批准号: 1516235
• 负责人: Daniel Dombeck
• 金额: $ 80万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1516235&HistoricalAwards=false
• 关键词: CRCNS; Functional; Imaging; Computational; Models

A fundamental question in neuroscience is to understand how different sets of neurons in a network combine or integrate their various inputs, and how both the pattern of inputs and the resulting outputs are related to behavior. Novel, even unprecedented, experimental methods have been and are being developed with which to image or record from large numbers of distributed neurons but understanding the integration step can be difficult since inputs and outputs are generally distributed over many millimeters, and it is very difficult to record from both simultaneously, especially so in a behaving animal. This project will combine experimental and computational methods to elucidate such synaptic integration in pyramidal neurons associated with mammalian spatial navigation in awake, behaving animals. The imaging data acquired from awake behaving mice will be made available to other groups and the full results will serve as a model for other research concerned with the integration of inputs in networks of neurons. The computational models will be made available on the ModelDB database and will be a resource to others working to understand other aspects of functionality in this brain region. Furthermore, the work will involve a close collaboration between experimental and computational research groups, thus giving postdoctoral fellows and graduate students cross-disciplinary research training.In pyramidal neurons of the hippocampus, the large dendritic tree constitutes an elaborate network of branching processes involving tens of thousands of excitatory synapses containing a variety of voltage-gated ion channels. The pattern of synaptic inputs impinging upon the dendritic arbor and the degree to which these inputs are processed by it to drive place field firing (i.e., firing correlated with spatial location) during behavior are currently unknown. The goals of the project are first to 1) develop improved computational models of dendritic place cell firing constrained by current imaging data and 2) establish new experimental techniques to image the inputs to pyramidal cells in the dendritic tree, at single spine resolution, during place field firing. Together the experiments and models will be used to 3) determine the degree to which local dendritic processing is involved in place cell firing. The proposed experiments will allow for the construction of significantly improved models of hippocampal function and the models will provide a framework within which to understand activity recorded at a local level in the dendritic tree and assemble a comprehensive picture of dendritic processing across the whole arbor.

神经科学中的一个基本问题是理解网络中不同的神经元集合是如何联合收割机组合或整合它们的各种输入的，以及输入的模式和最终的输出是如何与行为相关的。 新颖的，甚至是前所未有的，实验方法已经并正在开发，通过这些方法可以从大量的分布式神经元中成像或记录，但是理解整合步骤可能很困难，因为输入和输出通常分布在许多毫米上，并且很难同时记录两者，特别是在行为动物中。 这个项目将联合收割机实验和计算方法来阐明这种突触整合在锥体神经元与哺乳动物的空间导航在清醒，行为动物。 从清醒行为小鼠获得的成像数据将提供给其他小组，完整的结果将作为其他研究的模型，这些研究涉及神经元网络中输入的整合。计算模型将在ModelDB数据库中提供，并将成为其他人了解该大脑区域功能其他方面的资源。此外，实验研究小组和计算研究小组将密切合作，为博士后研究员和研究生提供跨学科的研究培训。在海马锥体神经元中，大型树突树构成了一个复杂的分支过程网络，涉及数万个含有各种电压门控离子通道的兴奋性突触。突触输入撞击树突乔木的模式以及这些输入被其处理以驱动位置场放电的程度（即，与空间位置相关的发射）的情况目前是未知的。该项目的目标首先是1）开发受当前成像数据约束的树突位置细胞放电的改进计算模型，2）建立新的实验技术，以在位置场放电期间以单棘分辨率对树突树中的锥体细胞的输入进行成像。这些实验和模型将一起用于3）确定局部树突处理参与位置细胞放电的程度。 拟议的实验将允许海马功能的显着改善模型的建设和模型将提供一个框架内，了解活动记录在局部水平的树突树和组装的树突处理在整个乔木的全面图片。