Rapid Network Reorganization With Learning

通过学习快速重组网络

• 批准号: 8433893
• 负责人: Angela Bruno
• 金额: $ 4.16万
• 依托单位: ROSALIND FRANKLIN UNIV OF MEDICINE & SCI
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-02-26 至 2014-08-25
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8433893
• 关键词: Aplysia; Biological Models; Biological Neural Networks; Brain; Data; Data Analyses; Development; Disease; Dyes; Generations; Goals; Image; Investigation; Learning; Locomotion; Mediating; Mental disorders; Methods; Modeling; Motor; Neurons; Pathology; Performance; Play; Preparation; Resolution; Role; Sorting - Cell Movement; Stimulus; Structure; Techniques; Testing; Time; Training; computerized data processing; flexibility; frontier; functional group; image processing; imaging modality; improved; learned behavior; memory encoding; nervous system disorder; neural circuit; novel; programs; relating to nervous system; tool; voltage

DESCRIPTION (provided by applicant): Neural ensemble dynamics - how groups of neurons shift between functional groups in real time to optimize neural network performance - represents a clinically important frontier in the study of brain function. Flexible ensemble structure is an inherent feature of many neural circuits, and we speculate that aberrations of such dynamics may contribute importantly to pathology in neurological and psychiatric disease. Unfortunately, technical issues have long hindered investigations of real-time flexibility in network structure. Here we will apply newly developed imaging and data processing tools to study flexible ensemble dynamics in an experimentally tractable model system - the Aplysia locomotion network. The central hypothesis is that Aplysia's locomotion network displays flexible ensemble structures during the course of its stimulus-elicited crawling motor program, which then persist to encode memory. The specific aims are to 1) define the rapid the reorganization of neural ensemble structures that occur during the time course of a single motor program, 2) determine to what extent the network ensembles identified in Aim 1 persist to encode the memory for sensitization, and 3) test the hypothesis that the rapid ensemble reorganization to be characterized in Aims 1 and 2 is mediated by known, intrinsic serotonergic modulatory neurons. The approach will use a powerful combination of large-scale imaging with fast voltage sensitive dyes and three data analysis techniques, including a fully automated spike-sorting method for identifying the neurons in the raw data, and two new-generation unsupervised spike train correlation methods to define and track the functional neuronal ensembles operating during normal network function and learning. The short term goal of this project is to provide a single-neuron resolution view of the moment-to-moment alterations in neural network structures that may be essential to healthy brain function. The long-term goal is to use such information to inform novel treatments for neurological and psychiatric disease.

描述(由申请人提供)：神经集成动力学--神经元组如何实时在功能组之间移动以优化神经网络性能--代表了大脑功能研究中的一个临床重要前沿。灵活的集合结构是许多神经回路的固有特征，我们推测这种动力学的异常可能在神经和精神疾病的病理中起重要作用。遗憾的是，技术问题长期以来一直阻碍对网络结构中实时灵活性的研究。在这里，我们将应用最新开发的成像和数据处理工具，在一个实验上易于处理的模型系统--海兔移动网络中研究柔性系综动力学。中心假设是，海兔的运动网络在其刺激引发的爬行运动程序的过程中显示出灵活的整体结构，然后这些结构持续编码记忆。其具体目的是：1)定义在单个运动程序的时间过程中发生的神经丛结构的快速重组；2)确定在目标1中确定的网络集合在多大程度上坚持对用于敏化的记忆进行编码；以及3)检验以下假设：在目标1和2中表征的快速集合重组是由已知的内在5-羟色胺能调制神经元介导的。该方法将使用大规模成像与快速电压敏感染料和三种数据分析技术的强大组合，包括用于识别原始数据中的神经元的全自动棘波分类方法，以及两种新一代无监督棘波序列关联方法，以定义和跟踪在正常网络功能和学习期间操作的功能神经元集合。该项目的短期目标是提供神经网络结构中可能对健康大脑功能至关重要的时刻变化的单个神经元分辨率视图。长期目标是利用这些信息为神经和精神疾病的新疗法提供信息。