Understanding Computation and Communication in the Brain

描述：（申请人提供） 每个人都知道大脑的作用是处理信息。但 那是什么意思？神经科学家应该如何量化这些信息 处理？当然，大脑不能被理解为一台数字计算机。和 尽管信息论对信号应该或 不应该在神经元之间传递，本身我们在神经元中发现了很多东西。 香农的基本信息理论似乎无法解释的大脑。为 例如，我们如何能够比较一种类型执行的计算 和另一种神经元的区别为什么某些解剖学和 一种类型的计算与另一种类型的计算相比， 计算？拟议的研究寻求适当的措施， 微观神经元功能，将使明智的定量方面， 神经元及其生理学。如果我们能成功地量化和测量 计算的方式，解释和预测有点不同的一套 定量观察，那么这些措施将有资格作为一个适当的 一种语言，用于量化神经系统执行的信息处理 系统 所提出的方法将融合信息理论与生物学 不可避免的问题;主要问题是计算成本， 通信为了建立相应的措施，研究将回答 例如：为什么静息电位在-70 mV左右？为什么不小一点; 为什么不更大？为什么能量浪费的静止电导没有变小？ 为什么不把大脑缩小一半，让它的计算速度提高一倍呢？为什么神经元 在观察到的频率范围内发生火灾？为什么突触失败会发生在一些 系统，而不是在其他人和什么是解释所观察到的量子 失败率？在回答这些问题的研究将推进一些 作为我们理解的渠道，同时取消其他措施。 这种资格，或取消资格，产生于成功的，或 不成功的不同生物数据集的定量匹配。的 基本的组织和相互联系的原则是：确定这些方面的 限制大脑信息处理的生物学。也就是说， 大脑是一个昂贵的器官：必须消耗食物和水来维持它的工作。 就其绝对大小而言，大脑是我们的负担， 随身携带。 在进行这样的研究，我们将使用数学分析，计算机为基础的 计算和生物物理模拟。所有这些工作都将利用 关于轴突、树突和突触的最基本数据， 文学这项拟议中的研究有望将各种各样的 解剖学和生理学观察，其中一些超过50年 古老的，很好的观察，经常使用，但从未完全解释过。拟议 研究必然是理论性的理论， 描述和理解信息处理的定量语言。 因为大脑的高级功能是由更简单的“计算”组成的 这样一个坚实的基础将有利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.