Understanding Computation and Communication in the Brain

描述：(申请人提供) 每个人都知道大脑的目的是处理信息。但 那是什么意思？神经学家应该如何量化这些信息？ 处理？当然，大脑不能理解为一台数字计算机。和 尽管信息论有一些关于信号应该如何或 不应该在神经元之间传递，就其本身而言，我们在 香农的基本信息论似乎无法解释的大脑。为 例如，我们将如何比较由一种类型执行的计算 神经元与其他类型神经元的区别？为什么某些解剖结构和 一种类型的计算比另一种类型的计算更适合生理学 计算？拟议的研究寻求适当的措施来量化 微观神经元功能，将使合理的量化方面 神经元及其生理学。如果我们能成功地量化和测量 以一种解释和预测一组有点不同的 定量观察，那么这些措施将被视为适当的 用于量化神经质执行的信息处理的语言 系统。 建议的方法将把信息论和生物学结合起来。 不可避免的问题；主要问题是计算成本和 沟通。为了建立适当的措施，研究将回答 这样的问题：为什么静息电位在-70 mV左右？为什么不能小一点呢？ 为什么不做大一点呢？为什么浪费能量的静止导体不更小呢？ 为什么不让计算速度减半的大脑快一倍呢？为什么神经元 在观察到的频率范围内有火灾吗？为什么有些人会出现突触故障 以及对观测到的量子的解释是什么 失败率呢？在回答这些问题时，研究将推进一些 这些措施是我们理解的渠道，但取消了其他措施的资格。 这种资格或取消资格源于成功，或 不同生物数据集的定量匹配不成功。这个 基本的组织和相互关联的原则是：确定以下方面 在数量上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维持它的工作 就其绝对大小而言，大脑对我们来说是一个负担 随身携带。 在进行这样的研究时，我们将使用数学分析，以计算机为基础 计算和生物物理模拟。所有这些工作都将借鉴 发表的关于轴突、树突和突触的大多数基本数据 文学。这项拟议的研究承诺将把不同的 解剖学和生理学观察，其中一些超过50年 陈旧的，仔细观察的，经常使用的，但从来没有完全解释过。建议数 研究必须是理论理论，才能产生 描述和理解信息处理的量化语言。 因为更高级的大脑功能是由更简单的计算组成的 这样一个坚实的基础将有助于神经科学家如何研究和理解 更高级的大脑功能。

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.