Modeling plasticity in neurons and neural circuits

神经元和神经回路的可塑性建模

• 批准号: 6233342
• 负责人: Douglas Alan Baxter
• 金额: $ 19.39万
• 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/6233342
• 关键词: Aplysia; action potentials; alternatives to animals in research; behavioral /social science research tag; computational neuroscience; mathematical model; model design /development; neural information processing; neural plasticity; nutrient intake activity; nutrition related tag

The overall objective of this research is to use computation models to analyze cellular and network processes underlying behavior and behavioral plasticity. Models will be based on empirical data from the neural circuitry that mediates feeding behavior of the marine mollusc Aplysia. This behavior is a useful model system for investigating the generation of complex rhythmic movements as well as the neural basis of motivational states and associative learning. Although many elements of the feeding circuitry have been identified, an analysis of the mechanisms underlying its overall function has not yet been undertaken. The present proposal outlines studies that will investigate the extent to which the current understanding of the circuitry is sufficient to account for features of the behavior, examine the contributions of component processes to neuronal and network dynamics, and examine how modulatory inputs and learning- induced changes in cellular properties alter the function of the circuit. In addition, this project will continue the development and distribution of the SNNAP program (Simulator for Neural Networks and Action Potentials), thereby providing a valuable research and educational tool to the scientific and academic community.

这项研究的总体目标是使用计算模型来分析行为和行为可塑性背后的细胞和网络过程。模型将基于介导海洋软体动物海兔摄食行为的神经回路的经验数据。这种行为是研究复杂节奏运动的生成以及动机状态和联想学习的神经基础的有用模型系统。尽管已经确定了馈电电路的许多元件，但尚未对其整体功能的机制进行分析。本提案概述了研究，这些研究将调查当前对电路的理解足以解释行为特征的程度，检查组成过程对神经元和网络动力学的贡献，并检查调节输入和学习引起的变化如何细胞特性改变了电路的功能。此外，该项目将继续开发和分发 SNNAP 程序（神经网络和动作电位模拟器），从而为科学和学术界提供有价值的研究和教育工具。