Understanding Computation and Communication in the Brain

了解大脑中的计算和通信

• 批准号: 6625978
• 负责人: WILLIAM B LEVY
• 金额: $ 18.5万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-04-19 至 2006-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6625978
• 关键词: bioenergetics; computational neuroscience; computer data analysis; computer simulation; human data; mathematics; model design /development; neural information processing; neurons; neuroregulation

DESCRIPTION:(provided by applicant) Everybody knows that the purpose of the brain is to process information. But what does that mean? How should neuroscientists quantify such information processing? Surely the brain cannot be understood as a digital computer. And although information theory has something to say about how signals should or should not be passed between neurons, by itself there is much we find in the brain that Shannon's basic information theory does not seem to explain. For example how will we be able to compare the computations performed by one type of neuron with those of another type of neuron? Why are certain anatomies and physiologies preferred for one type of computation versus another type of computation? The proposed research seeks appropriate measures to quantify microscopic neuronal function that will make sensible quantitative aspects of neurons and their physiology. If we can successfully quantify and measure computation in a way that explains and predicts a somewhat diverse set of quantitative observations, then these measures will qualify as an appropriate language for quantifying information processing performed by the nervous system. The proposed approach will merge information theory with biologically inescapable issues; the principle issue being the cost of computation and communication. To establish the appropriate measures, the research will answer questions such as: Why are resting potentials around -70 mV? Why not smaller; why not larger? Why aren't energetically wasteful resting conductances smaller? Why not have brains half the size that compute twice as fast? Why do neurons fire in the frequency ranges observed? Why do synaptic failures occur in some systems and not in others and what is the explanation for the observed quantal failure rates? In answering these questions the research will advance some measures as conduits of our understanding while disqualifying other measures. Such qualification, or disqualification, arises from successful, or unsuccessful, quantitative matching of different sets of biological data. The essential organizing and interrelating principle is: identify those aspects of biology that quantitatively limit information processing in the brain. That is, the brain is a costly organ: food and water must be consumed to keep it working properly and, in terms of its absolute size, the brain is a burden for us to carry around. In performing such research, we will use mathematical analysis, computer-based calculations, and biophysical simulations. All of this work will draw on the most basic data about axons, dendrites, and synapses in the published literature. The proposed research promises to tie together diverse sets of anatomical and physiological observations, some of which are over fifty years old, well observed, often used, but never fully explained. The proposed research is necessarily theoretical theory being what is needed to produce a quantitative language for describing and understanding information processing. Because higher brain functions are built out of simpler bits of' computation such a solid foundation will benefit how neuroscientists study and understand higher order brain functions.

描述：（由申请人提供） 大家都知道大脑的作用是处理信息。但 这意味着什么？神经科学家应该如何量化这些信息 加工？当然，大脑不能被理解为数字计算机。和 尽管信息论对信号应该如何或如何 不应该在神经元之间传递，就其本身而言，我们在 大脑认为香农的基本信息论似乎没有解释。为了 示例我们如何能够比较一种类型执行的计算 神经元与其他类型神经元的关系？为什么某些解剖学和 与另一种类型的计算相比，生理学更喜欢一种类型的计算 计算？拟议的研究寻求适当的措施来量化 微观神经元功能将使 神经元及其生理学。如果我们能够成功地量化和衡量 以一种解释和预测一组有些不同的方式进行计算 定量观察，那么这些措施将有资格作为适当的 用于量化神经执行的信息处理的语言 系统。 所提出的方法将信息论与生物学相结合 无法回避的问题；主要问题是计算成本和 沟通。为了制定适当的措施，研究将回答 诸如以下问题：为什么静息电位在 -70 mV 左右？为什么不更小？ 为什么不更大呢？为什么能量浪费的静息电导不更小？ 为什么不让大脑大小减半，计算速度提高两倍呢？为什么神经元 在观察到的频率范围内发生火灾？为什么有些人会发生突触故障 系统而不是其他系统以及观察到的量子的解释是什么 失败率？在回答这些问题时，研究将推进一些 措施作为我们理解的渠道，同时取消其他措施的资格。 此类资格或取消资格源于成功或 不同组生物数据的不成功的定量匹配。这 基本的组织和相互联系原则是：确定 定量限制大脑信息处理的生物学。那是， 大脑是一个昂贵的器官：必须消耗食物和水才能保持其运转 就其绝对大小而言，大脑是我们的负担 随身携带。 在进行此类研究时，我们将使用基于计算机的数学分析 计算和生物物理模拟。所有这些工作都将借鉴 已发表的有关轴突、树突和突触的最基本数据 文学。拟议的研究有望将不同的组合联系在一起 解剖学和生理学观察，其中一些超过五十年 古老，观察良好，经常使用，但从未得到充分解释。拟议的 研究必然是理论理论，是产生研究成果所需要的 用于描述和理解信息处理的定量语言。 因为更高级的大脑功能是由更简单的计算构建而成的 如此坚实的基础将有利于神经科学家的研究和理解 高阶大脑功能。