NETWORK, CELLULAR AND MOLECULAR DETERMINANTS OF LEARNING

学习的网络、细胞和分子决定因素

• 批准号: 2239900
• 负责人: John H Byrne
• 金额: $ 10.3万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 1986
• 资助国家: 美国
• 起止时间: 1986-09-30 至 1998-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2239900
• 关键词: Aplysia; SDS polyacrylamide gel electrophoresis; afferent nerve; alternatives to animals in research; association learning; computational neuroscience; electrophysiology; gene expression; genetic transcription; genetic translation; interneurons; learning; long term memory; molecular psychobiology; motor neurons; neural information processing; neural plasticity; neural transmission; neurofilament proteins; neurons; neurophysiology; phosphorylation; posttranslational modifications; protein biosynthesis; radiotracer; second messengers; short term memory; synapses

A fundamental problem in neuroscience is to understand events occurring within Individual neurons and within neural networks that contribute to forms of plasticity underlying learning and memory. This proposal outlines both empirical and modeling studies that will examine the molecular, biochemical and biophysical properties of Identified neurons and the connectivity of neural circuits that have demonstrated capacities for nonassociative and associative plasticity. Specifically, the neural circuit that mediates the tail withdrawal reflex will be analyzed. Many of the sensory neurons, Interneurons, motor neurons and modulatory interneurons that control this behavior have been Identified and are accessible to study. Thus, molecular, biochemical and cellular neurophysiological techniques will be applied to analyze the particular processes that might explain associative and nonassociative learning. Formalisms of the cellular and network processes that underlie these forms of plasticity will be developed and incorporated into quantitative, real-time models of neuron-like elements and neural networks. The ability of these models to fit the experimental data and to predict simple and complex features of learning will be examined. The proposed research will provide for a fairly complete analysis of the mechanisms underlying the Induction, expression and maintenance of simple forms of nonassociative and associative learning as well as help address fundamental questions regarding the mechanistic relationship between short- and long-term memories.

神经科学的一个基本问题是了解发生的事件 在单个神经元和神经网络中，有助于 可塑性的形式是学习和记忆的基础。 这个建议 概述了将检查的经验和建模研究 已鉴定神经元的分子，生化和生物物理特性 以及表现出能力的神经回路的连通性 用于非缔合性和联想性可塑性。 具体而言，神经 将分析介导尾部戒断反射的电路。许多 感觉神经元，中间神经元，运动神经元和调节性的 控制这种行为的中间神经元已被识别为 可以学习。 因此，分子，生化和细胞 神经生理技术将用于分析特定 可能解释联想和非社交学习的过程。 这些基础的细胞和网络过程的形式主义 可塑性形式将被开发并纳入定量中， 神经元元素和神经网络的实时模型。 这 这些模型适合实验数据并预测的能力 将检查简单而复杂的学习特征。 提议 研究将对机制进行相当完整的分析 简单形式的归纳，表达和维护的基础 非社交和联想学习以及帮助解决 有关机械关系的基本问题 短期和长期记忆。