COMPUTATIONAL APPROACH FOR STUDYING THE BRAIN

研究大脑的计算方法

• 批准号: 3387746
• 负责人: WILLIAM B LEVY
• 金额: $ 17.92万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-04-01 至 1994-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3387746
• 关键词: NMDA receptors; artificial intelligence; biological signal transduction; brain mapping; computer simulation; hippocampus; memory; neural information processing; neuroanatomy; statistics /biometry; synapses

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 as a function of synaptic modifications, neuronal physiologies, and the neuroanatomies of different brain regions. The proposed research concentrates on 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. By arriving at an abstract definition of the 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 neural network reflecting current knowledge of the hippocampus. This preprocessing for improving prediction is accomplished by recoding process that temporally compresses and statistically simplifies temporally distributed, polysensory signals. The methods of the research are computer simulations and theorem proving. By virtue of the abstract approach inherent in the use of information theoretic measures, it is possible to study input environments that are no more, or less, than environments defined by their statistics. The modeled brain networks transform sequences of inputs that are multivariate 0's and 1's into outputs that are also multivariate sequences of 0's and 1's. The statistics of the output sequences are then analyzed, and the information loss is evaluated. Additionally, the availability of the recoded information for a neuron-generated prediction is evaluated via a multivariate measure of statistical dependence. This evaluation is critical because the whole point of transforming signals - i.e., taking the information present in one representation of the input environment and recoding it as another representation of the same input - is to make a representation that is more usable by successive brain regions (minimizing statistical dependence can be very helpful). The computer simulations are repeated using a Monte Carlo procedure in order to quantify the average behavior of each network studied. Such results lead to conjectured theorems that, if possible, are rigorous statements concerning the average ability of each network to recode information. This recoding is specified in terms of information loss and statistical dependence and is a function of the statistics of the input environment, particular neuronal properties, and the anatomy under study (e.g. the synaptic modification rules, the existence of feedforward inhibition, etc).

治疗影响学习和的各种疾病的基础 记忆是对学习和记忆如何发生的理解。 那就是，现在 我们开始了解细胞和亚细胞事件 这导致突触修饰，是时候询问这种微观 更改被整合到功能性神经系统中并使用。 正如我们当前的知识所在，这远非显而易见的记忆 以时间分布的方式发生的多突空事件（例如， 我昨天下午在杂货店度过）可以召回和使用 通过大脑或多或少地编码事件。 这是我们的长期 目的是用 信息处理。 为了实现这一目标，拟议的研究 旨在创建定量理论，将信息处理定义为 突触修饰，神经元生理和 不同大脑区域的神经解剖学。 提出的研究集中于体系结构和细胞 海马的生理学目前被理解为 实际上可能是（考虑到生物学研究的限制 日期）。 准确的预测问题，如小动物所需的 在一项空间任务中，将注意力集中在特定问题上。 经过 达到预测问题的抽象定义，并通过 考虑神经系统的某些基本事实 计算复杂性理论，我们发展了预处理问题 神经网络中的信号反映了当前对 海马。 这项用于改进预测的预处理已完成 通过重新编码过程，从时间压缩和统计上简化 暂时分布的多功能信号。 该研究的方法是计算机模拟和定理证明。 借助信息的使用固有的抽象方法 理论措施，可以研究没有的输入环境 比其统计数据所定义的环境更多或更少。 建模 大脑网络转换多变量0的输入序列和 1分为输出，也是0和1的多元序列。 这 然后分析输出序列的统计信息，并信息 评估损失。 此外，重新编码的可用性 通过A评估神经元生成的预测的信息 统计依赖性的多元度量。 此评估是 至关重要，因为转换信号的重点 - 即 输入环境的一个表示中存在的信息和 将其重新编码为同一输入的另一种表示形式 - 是 由连续的大脑区域更可用的表示（最小化 统计依赖可能非常有帮助）。 计算机模拟是 使用蒙特卡洛程序重复以量化平均 每个网络的行为。 这样的结果导致猜想 如果可能的话，定理是关于平均的严格陈述 每个网络重新编码信息的能力。 指定了此重新编码 就信息丢失和统计依赖性而言，是一个功能 输入环境的统计数据，特定的神经元特性 以及正在研究的解剖学（例如，突触修饰规则， 喂食抑制的存在等）。