RELATING SYNAPTIC MODIFICATION TO COGNITIVE FUNCTION

治疗各种影响学习和学习的疾病的基础 记忆是对学习和记忆如何发生的理解。那是, 现在我们开始了解细胞和亚细胞 导致突触改变的事件，现在是时候问问这种情况是如何发生的 微观变化被整合到功能中，并被功能利用 神经系统。就我们目前所知，它远不是显而易见的。 如何存储在时间上发生的多感官事件的记忆 分配方式(例如，我昨天下午在杂货店度过 商店)可以被大脑回忆和使用，或多或少是离散的 编码事件。我们的长期目标是解释这些问题 在信息处理方面的学习和记忆。要完成 这一目标所提议的研究旨在创建一种量化理论 它将不同大脑区域的信息处理定义为 突触可修饰性的功能、神经生理学和 神经解剖学。 我将在接下来的五年里进行的研究既是试验性的 和理论上，尽管这个应用程序集中在理论上 调查。实验研究由基础研究部分组成。 海马体的生理学和解剖学研究，特别是 突触修饰的研究。理论研究分三个阶段进行 层次：1)单细胞生物物理学，将定量的 从解剖和生理学研究中获得的知识；2) 整合我们对蜂窝和亚蜂窝的知识的网络模型 加工成海马体的定量模型，就像 通过这个大脑区域进行信息处理；以及3)发展一种 孤立脑区信息处理的基本理论。 研究进展包括海马状网络的研究 能够使用抽象的信息测量来修改突触 正在处理。专业成长包括发展我的摘要 理论的方向是可以进行实验检验的。因此，我会 与研究动物行为和相互关联的科学家合作 单兵火力。 该提案描述了一种网络理论。这一不断发展的理论试图 定义和理解海马体中的信息处理。这个 本应用程序中讨论的研究隐含地假设 海马体的结构和细胞生理学 当前理解的或实际上可能理解的(考虑到 迄今为止进行的生物学研究)。精确度问题 预测，就像在空间任务中对小动物所要求的那样，专注于 我们对一个特定问题的关注，海马体是 切合实际。通过对任何预测得出抽象的定义 问题，并通过考虑神经系统的某些基本事实 在计算复杂性理论的背景下，我们发展了 类海马体神经网络中信号的预处理问题。 这种预处理是一种重新编码，它在时间上压缩和 统计上简化了时间分布的多感官信号。 此预处理提高了预测的质量 神经元和突触作为计算元素的限制。

Fundamental to curing a variety of disorders that affect learning and memory is an understanding of how learning and memory occur. That is, now that we are beginning to understand the cellular and subcellular events that lead to synaptic modification, it is time to ask how such microscopic changes are integrated into, and used by, the functioning nervous system. As our current knowledge stands, it is far from obvious how stored memories of polysensory events that occur In a temporally distributed manner (e.g., I spent yesterday afternoon at the grocery store) can be recalled and used by the brain as a more or less discretely coded event. It is our long-term objective to explain such issues of learning and memory in terms of information processing. To accomplish this objective the proposed research aims to create a quantitative theory that defines information processing in different brain regions as a function of synaptic modifiability, neuronal physiologies, and neuroanatomies. The research I will perform in the next five years is both experimental and theoretical although this application concentrates on the theoretical investigations. The experimental research consists of basic physiological and anatomical studies of the hippocampus, particularly studies of synaptic modification. The theoretical studies are at three levels: 1) single cell biophysics that integrate the quantitative knowledge obtained from the anatomical and physiological studies; 2) network models that integrate our knowledge of cellular and subcellular processes into quantitative models of the hippocampus in terms of information processing by this brain region; and 3) development of a basic theory of information processing for isolated brain regions. The research development includes studying hippocampal-like networks capable of synaptic modification using abstract measures of information processing. The professional growth includes developing my abstract theories in a direction that can be experimentally tested. Thus, I will collaborate with scientists who study behaving animals and correlated single-unit firing. The proposal describes a network theory. This developing theory seeks to define and understand information processing in the hippocampus. The research discussed in this application implicitly assumes the architectures and cellular physiologies of the hippocampus as they are currently understood or as they might actually be (given the limits of the biological research performed to date). The problem of accurate prediction, as is required of small animals in a spatial task, focuses our attention on a specific problem for which the hippocampus is relevant. By arriving at an abstract definition of any prediction problem and by considering certain basic facts of the nervous system in the context of the theory of computational complexity, we develop the issue of preprocessing signals in a hippocampal-like neural network. This preprocessing is a recoding that temporally compresses and statistically simplifies temporally distributed, polysensory signals. This preprocessing improves the quality of a prediction given the limitation of neurons and synapses as computational elements.