LIMBIC CORTICAL BASES OF ASSOCIATIVE LEARNING

这是NIMH研究科学家奖的申请， 对大脑机制的综合实验/理论研究 潜在的学习和记忆。 具体目标是制定一个 生物数学模型和计算机模拟 海马体的功能特性，大脑的一个区域， 对学习和记忆至关重要。 将采用三种方法， 实现这一目标：i）非线性系统分析方法将是 用于开发海马体的输入/输出模型，ii）场 理论方法将被用来开发一个详细的生理和 海马体的解剖模型，以及iii）用于 光学计算和超大规模集成电路设计将用于多个站点 海马体模型的记录和硬件表示。 的 由此产生的模型将用于解释与学习有关的活动， 海马神经元记录行为动物在经典 条件反射 在这些分析的基础上，希望能有一个更好的 理解将出现关于之间的关系， 网络动力学，突触可塑性，和信息存储在 个脑袋

This is an application for an NIMH Research Scientist Award to implement a combined experimental/theoretical study of the brain mechanisms underlying learning and memory. The specific goal is to develop a biologically based mathematical model and computer simulation of the functional properties of the hippocampus, a region of the brain which is essential for learning and memory. Three approaches will be used to reach this objective: i) a nonlinear systems analytic approach will be used to develop an input/output model of the hippocampus, ii) a field theory approach will be used to develop a detailed physiological and anatomical model of the hippocampus, and iii) technologies used in optical computing and VLSI design will be used for multiple site recording and hardware representations of models of the hippocampus. The resulting models will be used to interpret learning-related activity of hippocampal neurons recording from behaving animals during classical conditioning. On the basis of these analyses, it is hoped that a better understanding will emerge with respect to the relationships between network dynamics, synaptic plasticity, and information storage in the brain.