RELATING SYNAPTIC MODIFICATION TO COGNITIVE FUNCTION

治疗各种影响学习和 记忆是对学习和记忆如何发生的理解。 也就是说， 现在我们开始了解细胞和亚细胞 事件，导致突触修改，是时候问这样的 微观变化被整合到功能中，并被功能所使用。 神经系统 就我们目前的知识来看， 多感官事件的存储记忆是如何发生在一个暂时的 分布式方式（例如，我昨天下午在杂货店 存储）可以被大脑回忆和使用，作为一个或多或少的离散 编码事件。 我们的长期目标是解释这些问题， 学习和记忆在信息处理方面。 完成 这一目标，拟议的研究旨在建立一个定量理论， 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，神经元生理学， 神经解剖学 我将在未来五年进行的研究既是实验性的， 虽然本申请集中于理论上， 调查事务所 实验研究包括基础 海马的生理和解剖学研究，特别是 突触修饰的研究。 理论研究在三个 水平：1）单细胞生物物理学，整合定量 从解剖学和生理学研究中获得的知识; 2） 网络模型整合了我们对细胞和亚细胞的知识 处理成海马体的定量模型， 这个大脑区域的信息处理;和3）发展一个 孤立脑区信息处理的基本理论。 这项研究的进展包括研究类露营网络 能够利用抽象的信息测量来修改突触 处理. 专业成长包括发展我的抽象 理论的方向，可以通过实验来验证。 因此，我将 与研究动物行为的科学家合作， 单体射击 该提案描述了一种网络理论。 这一发展中的理论试图 定义和理解海马体中的信息处理。 的 本申请中讨论的研究隐含地假设 海马体的结构和细胞生理学 目前理解的或实际上可能的（考虑到 迄今为止进行的生物学研究）。 准确性问题 预测，作为小动物在空间任务中的要求， 我们的注意力集中在一个特定的问题上， 相关的 通过对任何预测的抽象定义 问题，并考虑到神经系统的某些基本事实， 在计算复杂性理论的背景下，我们开发了 在一个类帐篷神经网络预处理信号的问题。 这种预处理是一种重新编码， 在统计上简化了时间分布的多感觉信号。 这种预处理提高了预测的质量， 神经元和突触作为计算元件的局限性。

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.