NEURAL MODELS OF PLASTICITY: MOLECULAR TO NETWORKS

可塑性神经模型：分子到网络

• 批准号: 6187807
• 负责人: John H Byrne
• 金额: $ 99.22万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-08-25 至 2004-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6187807
• 关键词: computational neuroscience; neural plasticity

The main function of the nervous system is to process information in ways that lead to adaptive behavior, and to accomplish this, the excitability of neurons and the strength of their synaptic connections need to be modulated continually. After a neuron or neural system has been analyzed extensively, it becomes possible to ask what information it carries and how it contributes to this plasticity. At this point, there is generally so much data that only computational approaches can explain how individual components of a system interact, however. This Program Project will apply constructionistic computational techniques to several such well- characterized neural systems to achieve a more complete understanding of neuronal information processing and plasticity. The Project will examine multiple levels of organization, ranging from genetic networks within neurons to neural circuit. The individual projects will examine: 1) the dynamic properties and interactions of gene networks and excitable membranes; 2) the contribution of plasticity in individual neurons to associative learning; 3) the computational role of cellular and synaptic plasticity in an oscillatory neural circuit; and 4) the role of dopamine in light and dark adaptation in the primate retina. The individual projects are linked by a common goal of investigating plasticity in neurons and determining its contributions to higher levels of processing. For example, simulation of simple forms of cellular and synaptic plasticity may provide insights into the roles of these distinct mechanisms in the information processing capabilities of larger-scale neural networks such as those controlling feeding behavior. The group will be supported by a Computational Core that serves as a resource for developing models and for the exchange of information among the project groups. Another important goal of the project is to train graduate students and postdoctoral fellows in Computational Neuroscience. Finally, the Projects will further develop general-purpose simulation programs for neuronal and biochemical modeling, which will be used by the Program Project group. These programs will also be widely distributed to other groups who wish to apply computational approaches to analyze the properties of nerve cells and neuronal networks.

神经系统的主要功能是以导致适应性行为的方式处理信息，为了实现这一目标，神经元的兴奋性和突触连接的强度需要不断调节。在对一个神经元或神经系统进行了广泛的分析之后，就有可能问它携带了什么信息，以及它是如何促成这种可塑性的。在这一点上，通常有如此多的数据，只有计算方法才能解释系统的各个组件如何相互作用。本项目将运用建构计算技术来研究几个具有良好特征的神经系统，以更全面地了解神经元的信息处理和可塑性。该项目将研究多个层次的组织，从神经元内的遗传网络到神经回路。个别项目将研究：1)基因网络和可兴奋膜的动态特性和相互作用；2)个体神经元的可塑性对联想学习的贡献；3)细胞和突触可塑性在振荡神经回路中的计算作用；4)多巴胺在灵长类动物视网膜明暗适应中的作用。这些单独的项目都有一个共同的目标，即研究神经元的可塑性，并确定其对更高层次的处理的贡献。例如，对细胞和突触可塑性的简单形式的模拟可能会让我们深入了解这些不同机制在大型神经网络（如控制摄食行为的神经网络）的信息处理能力中的作用。该小组将由一个计算核心（Computational Core）提供支持，作为开发模型和在项目小组之间交换信息的资源。该项目的另一个重要目标是培养计算神经科学的研究生和博士后。最后，项目将进一步开发神经元和生化建模的通用仿真程序，这将由项目项目组使用。这些程序也将广泛分发给其他希望应用计算方法来分析神经细胞和神经网络特性的团体。